This paper addresses a critical issue in seabed characteri-zation with deep learning semantic segmentation using high-resolution Synthetic Aperture Sonar (SAS) data, that we call Catastrophic Receptive Field Overflow (CRFO). We propose novel methods, including Mosaic Augmentation and Homogeneous Patch Rejection, to (1) effectively mitigate CRFO and (2) enhance model performance. Through experiments on real-world SAS data, we investigate the origins of CRFO, revealing its dependence on model architectures and data characteristics. The presented solutions exhibit promising results, whether measured in terms of Overall Accuracy or the reliability of models in inference across various image input sizes or aspect ratios, in the face of new proposed metrics. These findings provide valuable insights for addressing CRFO challenges in tasks involving relatively homogeneous datasets.

Oriented object detection has a very wide range of application scenarios. In recent years, a lot of rotation detectors have been designed to achieve high-performance oriented object detection. Intersection-over-Union (IoU) is the commonly used indicator to evaluate the accuracy of detection performance. Many methods introduce IoU into the bounding box regression loss to achieve the aligned training and evaluation process for better performance. However, in this paper, we demonstrate several drawbacks of rotated IoU loss through both experiments and theoretical derivation: 1) There is a negative correlation between the loss gradient and the angular error. 2) The optimization process of rotated IoU loss suffers from scale sensitivity, which is not conducive to the model convergence. To solve the problems, we propose a Gradient Calibration Loss (GCL) that optimizes the rotated IoU loss via gradient analysis and correction. We construct the optimized gradient in GCL to avoid IoU loss oscillation and scale sensitivity, thereby accelerating model convergence. Models supervised by GCL have a more stable training process, faster convergence, and better performance. Moreover, GCL can be easily introduced into the existing rotation detectors to achieve performance gains without extra inference overhead. Extensive experiments on multiple oriented object detection datasets and models demonstrate the superiority of our method. Our method achieves state-of-the-art performance on the mainstream benchmark datasets. The source code and models are available at https://github.com/ming71/GCL.

Detecting oriented objects in optical remote sensing images has been consistently challenging due to difficulties in bounding boxes localization. The cascaded regression framework, widely employed for high-quality bounding box refinement, has demonstrated effectiveness in this domain. However, our experiments reveal a discontinuity issue in bounding box optimization in cascaded regression framework. As a result, performance gain is not guaranteed across all stages in this framework. In this paper, we propose a Distribution Discriminative Detector(DDDet) to address the above issues and enhance the optimization of bounding boxes in oriented object detection. Specifically, a novel Conditional Anchor Refinement Framework(CARF) is designed to improve cascaded regression structure. CARF distinguishes bounding boxes with different distributions, adaptively optimizing them within the well-assigned regressors. Subsequently, the Aligned Convolution Module(ACM) is integrated into each regressor, facilitating the continuous alignment between features and refined anchors. Furthermore, the Geometry-guided Training Sample Selection(GTSS) method is incorporated into CARF to assign labels based on object shape priors. Experimental results show that DDDet obtains state-of-the-art performance on mainstream datasets for oriented object detection in remote sensing image, which demonstrates the effectiveness of the proposed method. Our method surpasses many current single-stage detectors, two-stage detectors, and refine-stage detectors, achieving the mAP of 79.41% on DOTA dataset, and 44.15% on FAIR1M dataset.

Seabed characterisation consists in the study of the physical and biological properties of the bottom of the oceans. It is effectively achieved with sonar, a remote sensing method that captures acoustic backscatter of the seabed. Classical Machine Learning (ML) and Deep Learning (DL) research have failed to successfully address the automatic mapping of the seabed from noisy sonar data. This work introduces the Deep Supervised Semantic Segmentation model for Seabed Characterisation (D4SC), a novel U-Net-like model tailored to such data and low-label regime, and proposes a new end-to-end processing pipeline for seabed semantic segmentation. That dual contribution achieves state-of-the-art results on a high resolution Synthetic Aperture Sonar (SAS) survey dataset.

Band selection (BS) reduces effectively the spectral dimension of a hyperspectral image (HSI) by selecting relatively few representative bands, which allows efficient processing in subsequent tasks. Existing unsupervised BS methods based on subspace clustering are built on matrix-based models, where each band is reshaped as a vector. They encode the correlation of data only in the spectral mode (dimension) and neglect strong correlations between different modes, i.e., spatial modes and spectral mode. Another issue is that the subspace representation of bands is performed in the raw data space, where the dimension is often excessively high, resulting in a less efficient and less robust performance. To address these issues, in this article, we propose a tensor-based subspace clustering model for hyperspectral BS. Our model is developed on the well-known Tucker decomposition. The three factor matrices and a core tensor in our model encode jointly the multimode correlations of HSI, avoiding effectively to destroy the tensor structure and information loss. In addition, we propose well-motivated heterogeneous regularizations (HRs) on the factor matrices by taking into account the important local and global properties of HSI along three dimensions, which facilitates the learning of the intrinsic cluster structure of bands in the low-dimensional subspaces. Instead of learning the correlations of bands in the original domain, a common way for the matrix-based models, our model learns naturally the band correlations in a low-dimensional latent feature space, which is derived by the projections of two factor matrices associated with spatial dimensions, leading to a computationally efficient model. More importantly, the latent feature space is learned in a unified framework. We also develop an efficient algorithm to solve the resulting model. Experimental results on benchmark datasets demonstrate that our model yields improved performance compared to the state-of-the-art.

Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, where the two tasks are decoupled, the denoising operation does not optimally serve the classification task and sometimes even deteriorates it. We introduce here a unified deep learning framework for joint denoising and classification of high-dimensional images, and we particularly apply it in the framework of hyperspectral imaging. Earlier works on joint image denoising and classification are very scarce, and to the best of our knowledge, no deep learning models were proposed or studied yet for this type of multitask image processing. A key component in our joint learning model is a compound loss function, designed in such a way that the denoising and classification operations benefit each other iteratively during the learning process. Hyperspectral images (HSIs) are particularly challenging for both denoising and classification due to their high dimensionality and varying noise statistics across the bands. We argue that a well-designed end-to-end deep learning framework for joint denoising and classification is superior to current deep learning approaches for processing HSI data, and we substantiate this by results on real HSI images in remote sensing. We experimentally show that the proposed joint learning framework substantially improves the classification performance compared to the common deep learning approaches in HSI processing, and as a by-product, the denoising results are enhanced as well, especially in terms of the semantic content, benefiting from the classification.

Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as hyperspectral image (HSI), while, nowadays, multisensor data are being routinely acquired. In this article, we propose a multiview subspace clustering model that exploits effectively the rich information from multiple features extracted either from a single data source (HSI) or from multiple sources that we call generically multiviews of the same scene. An important novelty of our approach is that it integrates local and nonlocal spatial information from each view in a unified framework. Our model learns a common intrinsic cluster structure from view-specific subspace representations by a new decomposition-based scheme. In addition, we develop innovative manifold-based spatial regularization as a hybrid hypergraph, which merges local and nonlocal spatial context and improves, thereby, the learning of view-specific structures. We develop an efficient algorithm to solve the resulting optimization problem. Extensive experiments on real data sets demonstrate the superior clustering performance over the state of the art.

Band selection, which removes irrelevant bands from hyperspectral images (HSIs) and keeps essential spectral information contained in a relatively few bands, allows huge savings in data storage, computation time, and imaging hardware. In this article, we propose a novel structural subspace clustering (STSC) method for hyperspectral band selection, which leverages the self-representation property of data and structural prior information to learn the cluster structure of bands. Particularly, we propose a general clustering model where the coarse coefficients matrix derived from a self-representation model is decomposed as a combination of a desirable coefficients matrix and a sparse matrix. This strategy adaptively adjusts the coarse coefficients matrix to learn the intrinsic data structure in low-dimensional subspaces. To guide this learning process, we introduce a structural regularization approach which makes use of the prior information about local and global properties of spectral bands. Moreover, we incorporate also prior knowledge about the dictionary, which demonstrates to yield a better clustering performance. We develop an adaptive method to estimate the number of selected bands by analyzing eigenvalue gaps of Laplacian matrix. To solve the resulting model, an efficient algorithm based on alternating direction method of multipliers (ADMMs) is developed. Extensive experiments on benchmark HSIs show that our method outperforms the state-of-the-art band selection methods.

Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input HSI data as a dictionary of atoms, in terms of which all the data samples are linearly represented. This leads to highly redundant dictionaries of huge size, and the computational complexity of the resulting optimization problems becomes prohibitive for large-scale data. In this article, we propose a scalable subspace clustering method, which integrates the learning of a concise dictionary and robust subspace representation in a unified model. This reduces significantly the size of the involved optimization problems. We introduce a new adaptive spatial regularization for the representation coefficients, which incorporates spatial information of HSI and improves the robustness of the model to noise. We derive an effective solver based on alternating minimization and alternating direction method of multipliers (ADMMs) to solve the resulting optimization problem. Experimental results on four representative hyperspectral images show the effectiveness of the proposed method and excellent clustering performance relative to the state of the art.

Convolutional neural network (CNN) has been widely applied in hyperspectral image (HSI) classification exhibiting excellent performance. Weak generalization of CNN models to different datasets is a common issue in this domain largely because of limited amount of labeled training samples. In this article, we propose afullygroup convolutional neural network (FGCNN) method that integrates cascades of shuffled group convolutions tailored to different network stages. To our knowledge, this is the first reported full-group CNN model in general, and we design it in particular for robust spectral–spatial classification of HSI. In the primary feature extraction stage, we develop an original multiscale spectral feature extraction approach based on a novel concept of multikernel depthwise convolution that we define in terms of shuffled and importance-weighted group convolution. In the subsequent stage, we introduce a discriminative spectral–spatial feature extraction method with a novel group competition block to capture informative features with relatively few parameters. The final feature fusion stage is defined as a novel lightweight group feature fusion method that sharply reduces fusion weights compared to traditional methods with fully connected layers. Experimental results on three datasets show that the proposed FGCNN yields robust classification accuracy under the same hyperparameter settings compared to the current state-of-the-art.

Recent progress in spectral classification is largely attributed to the use of convolutional neural networks (CNNs). While a variety of successful architectures have been proposed, they all extract spectral features from various portions of adjacent spectral bands. In this article, we take a different approach and develop a deep spectral feature fusion method, which extracts both local and interlocal spectral features, capturing thus also the correlations among nonadjacent bands. To our knowledge, this is the first reported deep spectral feature fusion method. Our model is a two-stream architecture, where an intergroup and a groupwise spectral classifier operate in parallel. The interlocal spectral correlation feature extraction is achieved elegantly, by reshaping the input spectral vectors to form the so-called nonadjacent spectral matrices. We introduce the concept of groupwise band convolution to enable the efficient extraction of discriminative local features with multiple kernels adopting the local spectral content. Another important contribution of this work is a novel dual-channel attention mechanism to identify the most informative spectral features. The model is trained in an end-to-end fashion with a joint loss. Experimental results on real datasets demonstrate excellent performance compared with the current state of the art.

Recent progress in spectral classification is dominated by the use of deep learning models. While various learning architectures have been developed, they all extract spectral features from a single view input. In this paper, we investigate a different perspective and develop a unified multiview spectral feature learning framework, which extracts discriminative spectral features from multiple views of inputs. To our knowledge, this is the first reported multiview spectral feature learning method based on deep learning. In this framework, we introduce a multiview spectrum construction method by transforming the input spectral vector into multiple 3D image patches with different sizes, termed as multiview spectrum. This multiview spectrum is fed to a well-designed triple-stream architecture, where a global and two local spectral feature learning networks operate in parallel, capturing thus both global and local spectral contextual features simultaneously. Another important contribution of this work is a novel interactive attention mechanism to identify the most informative spectral contextual features. The model is trained in an end-to-end fashion from scratch with a joint loss. Experimental results on four data sets demonstrate excellent performance compared to the current state-of-the-art.

Conditional Variational Auto Encoders (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. The codes in the latent space provide a theoretically sound way to produce counterfactuals, i.e. alterations resulting from an intervention on a targeted semantic feature. To be applied on real images more complex models are needed, such as Hierarchical CVAE. This comes with a challenge as the naive conditioning is no longer effective. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals and we introduce VAEX1an Hierarchical VAE designed for this approach that can visually audit a classifier in applications.

Over time, crack pattern (craquelure) inevitably develops in paintings as a sign of their ageing, sometimes accompanied by larger losses of paint (lacunas). In restoration treatments, cracks are typically not filled in, and virtual restoration is often the only option to “reverse” the ageing of paintings, simulating their original appearance. Moreover, virtual restoration can serve as an important supporting step in decision making during the physical restoration. In this research, we investigate the possibility of applying deep learning-based methods for virtual restoration. In particular, our crack detection method is based on a convolutional autoencoder (U-Net), and we employ a generative adversarial neural network (GAN) to virtually inpaint the detected cracks. We propose an original way of training the GAN model for painting restoration, which improves its practical performance. A series of experiments shows encouraging results in comparison with known methods, and indicates huge potential of deep learning for virtual painting restoration.

With the ever-increasing availability of data, the need for efficient and accurate image retrieval methods has become larger and larger. Deep hashing has proven to be a promising solution, by defining a hash function to convert the data into a manageable lower-dimensional representation. In this paper, we apply recent insights from the field of variational autoencoders to the field of deep image hashing, thus achieving an improvement over the current state of the art as shown by experimental evaluation. The code used in this paper is open-source and available on GitHub (https://github.com/maximverwilst/deepimagehashing-VAE).

Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in clustering of hyperspectral images. However, the computational complexity of SSC-based methods is prohibitive for large-scale problems. We propose a large-scale SSC-based method, which processes efficiently large-scale HSIs without sacrificing the clustering accuracy. The proposed approach incorporates sketching of the self-representation dictionary reducing thereby largely the number of optimization variables. In addition, we employ a total variation (TV) regularization of the sparse matrix, resulting in a robust sparse representation. We derive a solver based on the alternating direction method of multipliers (ADMM) for the resulting optimization problem. Experimental results on real data show improvements over the traditional SSC-based methods in terms of accuracy and running time.

In this paper, we propose a Variational Auto-Encoder able to correctly reconstruct a fine mesh from a very low-dimensional latent space. The architecture avoids the usual coarsening of the graph and relies on pooling layers for the decoding phase and on the mean values of the training set for the up-sampling phase. We select new operators compared to previous work, and in particular, we define a new Dirac operator which can be extended to different types of graph structured data. We show the improvements over the previous operators and compare the results with the current benchmark on the Coma Dataset.

In this paper, we present a novel approach for designing local image descriptors that learn from data and from hand-crafted descriptors. In particular, we construct a learning model that first mimics the behaviour of a hand-crafted descriptor and then learns to improve upon it in an unsupervised manner. We demonstrate the use of this knowledge-transfer framework by constructing the learned BRIEF descriptor based on the well-known hand-crafted descriptor BRIEF. We implement our learned BRIEF with a convolutional autoencoder architecture. Evaluation on the HPatches benchmark for local image descriptors shows the effectiveness of the proposed approach in the tasks of patch retrieval, patch verification, and image matching.

The representation power of convolutional neural network (CNN) models for hyperspectral image (HSI) analysis is in practice limited by the available amount of the labeled samples, which is often insufficient to sustain deep networks with many parameters. We propose a novel approach to boost the network representation power with a two-stream 2-D CNN architecture. The proposed method extracts simultaneously, the spectral features and local spatial and global spatial features, with two 2-D CNN networks and makes use of channel correlations to identify the most informative features. Moreover, we propose a layer-specific regularization and a smooth normalization fusion scheme to adaptively learn the fusion weights for the spectral-spatial features from the two parallel streams. An important asset of our model is the simultaneous training of the feature extraction, fusion, and classification processes with the same cost function. Experimental results on several hyperspectral data sets demonstrate the efficacy of the proposed method compared with the state-of-the-art methods in the field.

Sparse representation based methods have demonstrated their superior performance in target detection tasks compared to more traditional approaches such as matched subspace detectors and adaptive subspace detectors. However, the existing sparsity-based target detection methods were mostly formulated for and validated on a single imaging modality (sometimes with multiple spectral bands). In many application domains, including art investigation, multimodal data, acquired by different sensors are readily available, and yet, efficient processing techniques for such data are still scarce. In this paper, we propose a sparsity-based multimodal target detection method that processes jointly the information from multiple imaging modalities in a kernel feature space, and making use of the spatial context. We develop our target detector such to be robust to errors in labelled data, which is especially important in applications like digital painting analysis, where pixel-wise manual annotations are unreliable. We apply the proposed method to a challenging application of paint loss detection in master paintings and we demonstrate its effectiveness on a case study with multimodal acquisitions of the Ghent Altarpiece.

Convolutional Neural Network (CNN) has been widely applied in hyperspectral image (HSI) classification exhibiting excellent performance. The CNN model overfitting is a common issue in this domain due to limited amount of labelled training samples. In addition, making the full use of spectral information is still considered an open problem. In this paper, we propose a novel group 2D-CNN model for spectral-spatial classification. Specifically, we propose an original multi-scale spectral feature extraction approach based on a novel concept of multi-kernel depthwise convolution. Furthermore, we exploit for the first time shuffle operation on the group convolutions in HSI spectral-spatial feature extraction to effectively limit the amount of learning parameters. As a result, we design a small and efficient network for HSI classification. Experimental results on real data demonstrate favourable performance compared to the current state-of-the-art.

Specular reflections are undesirable phenomena that can impair overall perception and subsequent image analysis. In this paper, we propose a modern solution to this problem, based on the latest achievements in this field. The proposed method includes three main steps: image enhancement, detection of specular reflections, and reconstruction of damaged areas. To enhance and equalize the brightness characteristics of the image, we use the alpha-rooting method with an adaptive choice of the optimal parameter-alpha. To detect specular reflections, we apply morphological filtering in the HSV color space. At the final stage, there is a reconstruction of damaged areas using adversarial neural networks. This combination makes it possible to quickly and effectively detect and remove specular reflections, which is confirmed by a series of experiments given by the experimental section of this work.

Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in the clustering of hyperspectral images (HSIs). However, the high computational complexity and sensitivity to noise limit its clustering performance. In this paper, we propose a scalable SSC method for the large-scale HSIs, which significantly accelerates the clustering speed of SSC without sacrificing clustering accuracy. A small landmark dictionary is first generated by applying k-means to the original data, which results in the significant reduction of the number of optimization variables in terms of sparse matrix. In addition, we incorporate spatial reg-ularization based on total variation (TV) and improve this way strongly robustness to noise. A landmark-based spectral clustering method is applied to the obtained sparse matrix, which further improves the clustering speed. Experimental results on two real HSIs demonstrate the effectiveness of the proposed method and the superior performance compared to both traditional SSC-based methods and the related large-scale clustering methods.

We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intraclass variance while maximizing the interclass separability to the class label space. However, standard metric learning techniques do not incorporate the class interaction information in learning the transformation matrix, which is often considered to be a bottleneck while dealing with fine-grained visual categories. As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present at the nonleaf nodes of the tree. We employ an iterative maximum-margin clustering strategy to obtain the hierarchical organization of the classes. Experiment results obtained on the large-scale NWPU-RESISC45 and the popular UC-Merced data sets demonstrate the efficacy of the proposed hierarchical metric learning-based RS scene recognition strategy in comparison to the standard approaches.

Sparse subspace clustering (SSC) has been widely applied in remote sensing demonstrating excellent performance. Recent extensions incorporate spatial information, typically via smoothness-enforcing regularization. We propose an alternative approach: a joint sparsity SSC model, where pixels within a local region are enforced to select a common set of samples in the subspace-sparse representation. The corresponding optimization problem is solved by the alternating direction method of multipliers (ADMM). Experimental results on real data show a significant improvement over SSC and related state-of-the-art methods.

Sparsity-based classification methods have been widely used in hyperspectral image (HSI) classification. These methods typically assumed Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust super-pixel level joint sparse representation classification model (RSJSRC) to address the mixed noise problem in sparsity-based HSI classification. Our method takes into account both Gaussian and sparse noise. Experimental results on simulated and real data demonstrate the efficiency of the proposed method and clear benefits from the introduced mixed-noise model.

Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifically, the proposed algorithm enhances the sparsity of fractional abundances in both spectral and spatial domains through the use of double weights, where one is used to enhance the sparsity of endmembers in spectral library, and the other is introduced to improve the sparsity of fractional abundances. Moreover, a TV-based regularization is further adopted to explore the spatial-contextual information. As such, the simultaneous utilization of both double reweighted l1minimization and TV regularizer can significantly improve the sparse unmixing performance. Experimental results on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm both visually and quantitatively.

This letter introduces a new spectral-spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such that the pixel-level data can be represented by multiscale superpixel-level (MSP) data sets. Then, a subspace-based support vector machine (SVMsub) is adopted to obtain the classification maps with multiscale inputs. Finally, the classification result is achieved via a decision fusion process. The resulting method, called MSP-SVMsub, makes use of the spatial and spectral coherences, and contributes to better feature characterization. Experimental results based on two real hyperspectral data sets indicate that the MSP-SVMsub exhibits good performance compared with other related methods.

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.

Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through double weights, in which one is used to enhance the sparsity of endmembers in the spectral library, and the other to improve the sparseness of abundance fraction of every material. Experimental results on both synthetic and real hyperspectral data sets demonstrate effectiveness of the proposed method both visually and quantitatively.

Presents a listing of the new Associate Editors for this issue of the publication.

Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.

In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure divides the image into variable size blocks according to their context, constraining thereby the search for candidate patches to nonlocal image regions with matching context. This approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes a priori knowledge about consistency of neighboring image patches. We solve the resulting optimization problem with an efficient low-complexity inference method. Experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch, text, and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident.

This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyper-spectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively.

In this paper we give new insights into quantitative microwave tomography with robust Huber regularizer and Gauss-Newton optimization. Firstly, we validate this approach for the first time on real electromagnetic measurements. Secondly, we extend the framework with a modified Huber function, which behaves like TV regularization. This is interesting for reconstructing piece-wise constant permittivities that appear in non-destructive testing of installations and other man-made objects.

Wavelet-based demosaicing techniques have the advantage of being computationally relatively fast, while having a reconstruction performance that is similar to state-of-the-art techniques. Because the demosaicing rules are linear, it is fairly simple to integrate denoising into the demosaicing. In this paper, we present a method that performs joint denoising and demosaicing, using a Gaussian Scale Mixture (GSM) prior model, thereby modeling the local edge direction as a hidden variable. The results indicate that this technique offers a better reconstruction performance (in PSNR sense and visually) than sequential demosaicing and denoising. On a recent GPU, our algorithm takes 3.5 s for reconstructing a 12 megapixel RAW digital camera image.

This paper presents a two-stage denoising method for hyper-spectral image (HSI) by combining kernel principal component analysis (KPCA) and total variation (TV). In the first stage, we use KPCA denoising to reduce spectrally uncorre-lated noise. In the second stage, the information content is largely separated from the remaining noise by means of principal component analysis (PCA). The remaining noise is then efficiently removed by fast primal-dual TV denoising in low-energy PCA channels. Experimental results on simulated and real HSIs are very encouraging.

We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.

In the field of image denoising, the non-local means (NLMS) filter is a conceptually simple, yet powerful technique. This filter exploits non-local, i.e. spatially repetitive, structure in natural images to estimate noise-free structure. In contrast, a wide variety of image restoration problems have been solved exploiting local smoothness of natural images, e.g. by enforcing sparsity of images when subjected to a multi-resolution transform. In this paper we introduce the prior knowledge of non-local repetitiveness of image structures into a broad multi-resolution image restoration framework. The proposed framework allows the power of the NLMS filter, supplemented by multi-resolution sparsity, to be extended for a wide variety of image restoration problems, such as demosaicing, deconvolution, reconstruction from insufficient measurements,... in a conceptually simple way.

In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative microwave tomography aims at estimating the permittivity profile of a scattering object based on measured scattered fields, which is a nonlinear, ill-posed inverse problem. The results on 3D data sets are encouraging: the reconstruction error is reduced and the permittivity profile can be estimated from fewer measurements compared to state-of-the art inversion procedures.

In this paper we present an efficient way to both compute and extract salient information from trace transform signatures to perform object identification tasks. We also present a feature selection analysis of the classical trace-transform functionals, which reveals that most of them retrieve redundant information causing misleading similarity measurements. In order to overcome this problem, we propose a set of functionals based on Laguerre polynomials that return orthonormal signatures between these functionals. In this way, each signature provides salient and non-correlated information that contributes to the description of an image object. The proposed functionals were tested considering a vehicle identification problem, outperforming the classical trace transform functionals in terms of computational complexity and identification rate.

In this paper, we present a first-order primal-dual algorithm for tackling the joint demosaicking and deconvolution problem. The proposed algorithm exploits the sparsity of both discrete gradient (TV) and shearlet coefficients as prior knowledge. In order to deal with this sparsity across the color channels, we first decorrelate the signals in color space before sparsifying them spatially, resulting in a separable transform. We demonstrate that this approach yields better results than employing group sparsity strategies. We propose to update the decorrelation operator during the image reconstruction, this approach will result in a significant improvement in PSNR. By relaxing the sparsity of the chrominance signals, we obtain both better objective and subjective image quality compared to other state-of-the-art demosaicking and deconvolution algorithms. Also, color artifacts due to demosaicking are suppressed very well.

In this paper, we propose a novel global Markov Random Field based image inpainting method with context-aware label selection. Context is determined based on the texture and color features in fixed image regions and is used to distinguish areas of similar content to which the search for candidate patches is limited. Furthermore, we introduce a novel optimization approach, as an alternative to priority belief propagation framework, which further reduces the number of candidates and performs efficient inference to obtain final inpainting result. Experimental results show improvement over related state-of-the-art methods. Moreover, global optimization is significantly accelerated with the proposed inference approach.

In this paper, we present a new method for High Dynamic Range (HDR) reconstruction based on a set of multiple photographs with different exposure times. While most existing techniques take a deterministic approach by assuming that the acquired low dynamic range (LDR) images are noise-free, we explicitly model the photon arrival process by assuming sensor data corrupted by Poisson noise. Taking the noise characteristics of the sensor data into account leads to a more robust way to estimate the non-parametric camera response function (CRF) compared to existing techniques. To further improve the HDR reconstruction, we adopt the split-Bregman framework and use Total Variation for regularization. Experimental results on real camera images and ground-truth data show the effectiveness of the proposed approach.

Objective characterization of jewels in paintings, especially pearls, has been a long lasting challenge for art historians. The way an artist painted pearls reflects his ability to observing nature and his knowledge of contemporary optical theory. Moreover, the painterly execution may also be considered as an individual characteristic useful in distinguishing hands. In this work, we propose a set of image analysis techniques to analyze and measure spatial characteristics of the digital images of pearls, all relying on the so called spatiogram image representation. Our experimental results demonstrate good correlation between the new metrics and the visually observed image features, and also capture the degree of realism of the visual appearance in the painting. In that sense, these results set the basis in creating a practical tool for art historical attribution and give strong motivation for further investigations in this direction.

In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.

Parallel Imaging MRI (pMRI) and Compressive Sensing (CS) are two reconstruction techniques that have recently been applied to increase MRI performance. In this paper we demonstrate that a combined analysis of the pMRI and CS problems leads to a conceptually simple, yet effective technique that outperforms independent approaches to both reconstruction problems. We argue that the proposed technique is also naturally resilient to noise, due to its relation to the MAP image denoising formulation. A modified Basis Pursuit (BP) formulation of the CS-MRI problem allows it to handle the pMRI problem at the same time. We also present an exact solution to this BP problem, using the split Bregman technique, with discrete shearlet transform (DST) regularization. The DST is an excellent choice for natural image applications, due to its optimal sparsity property. Results show that this Compressive Parallel Sensing (COMPASS) reconstruction algorithm outperforms more traditional MRI reconstruction algorithms in both pMRI and CS experiments.

A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyperspectral images. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which solves the eigenvectors through iteration. Without performing eigen decomposition on Gram matrix, our method can reduce the space complexity and time complexity greatly. Experimental results were validated in comparison with the standard KPCA and linear version methods.

In this paper, we derive a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square sense. The main idea is to use the total least square metrics instead of the ordinary least square metrics, which is commonly used in the literature. While the OLS model indicates that the target image may contain noise and the reference image should be noise-free, this puts a severe limitation on practical registration problems. By introducing the TLS model, which allows perturbations in both images, we can obtain mutually consistent parameters. Experimental results show that our method is indeed much more consistent and accurate in presence of noise compared to existing registration algorithms.

The dual-tree complex wavelet transform (DT-CWT) uses approximate Hilbert transform pairs of wavelet, which requires that the filters of each tree of the dual-tree structure should be delayed approximately one half sample from each other. However, the filters of the first (finest) scale of the transform do not obey this condition, resulting in a poor directional selectivity for the first scale. In this paper, we describe a design technique for first-scale infinite impulse response (IIR) wavelet filters, that solves this problem. Results demonstrate that a much better directional selectivity is obtained, which indicates a performance improvement for many applications that use the DT-CWT where the preservation of high-frequency information is important.

Integrating video coding and denoising is a novel processing paradigm, bringing mutual benefits to both video processing tools. In this paper, we propose a novel video denoising approach of which the main idea is reusing motion estimation resources from the video coding module for video denoising. In most cases, the motion fields produced by real-time video codecs cannot be directly employed in video denoising, since they, as opposed to noise filters, tolerate errors in the motion field. In order to solve this problem, we propose a novel motion-field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising. Additionally, a novel temporal filter is proposed that is robust against errors in the estimated motion field. Numerical results demonstrate that the proposed denoising scheme is of low-complexity and compares favorably to the state-of-the-art video denoising methods.

Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the "signal of interest" and that is applicable to correlated noise. We combine the intrascale model with a hidden Markov tree model to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.

We propose a new statistical model for image restoration in which neighborhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighborhood is obtained, thereby modeling the strongest correlations in that neighborhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However, the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.

In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.

In image denoising applications, noise is often correlated and the noise energy and correlation structure may even vary with the position in the image. Existing noise reduction and estimation methods are usually designed for stationary white Gaussian noise and generally work less efficient in this case because of the noise model mismatch. In this paper, we propose an EM algorithm for the estimation of spatially variant (nonstationary) correlated image noise in the wavelet domain. In particular, we study additive white Gaussian noise filtered by a space-variant linear filter. This general noise model is applicable to a wide variety of practical situations, including noise in Computed Tomography (CT). Results demonstrate the effectiveness of the proposed solution and its robustness to signal structures.

In this paper we present a novel method for detection of individual C.Elegans worms in population images in presence of overlapping. First, in a pre-processing phase the worms skeletons are obtained by morphological skeleton operation after image binarization and filling small holes. Then, after pruning the small branches of the skeletons, the skeletons are splited into several branches from the pixels with more than two neighbors. Angle of each branch side is calculated in the next stage and the neighbor branches with angle difference less than a predefined threshold are merged. Finally, a simple post-processing based on stastical analysis of worms' length on their widths is used in order to increase the automatic efficiency of the method. We have applied our method to a database of 147 isolated and overlapped worms and obtained 81.43% accuracy.

This letter presents a novel locally adaptive error concealment method for subband coded images. For each lost low-frequency coefficient, we estimate the optimal interpolation weights from its neighborhood. The calculation of the interpolation weights is optimized in the mean squared error sense, and it takes into account the errors that would arise by horizontally and vertically interpolating the available neighbors of the lost coefficient. Compared to methods of similar complexity, the proposed scheme estimates the lost coefficients more accurately: on average, the PSNR is increased by up to 4.5 dB. The reconstructed images also look better, and our method is fast and of low complexity.

In image communication over lossy packet networks (e.g., cell phone communication), packet loss errors lead to damaged images. Damaged images can be repaired with passive error concealment methods, which use neighboring coefficient or pixel values to estimate the missing ones. Neighboring image data should, thus, be spread over different packets. This paper presents a novel robust packetization method for the transmission of image content in lossy packet networks. We first define novel criteria for a good packetization. Based on these properties, we propose a cost function for packetization masks. We then use stochastic optimization to calculate optimal packetization masks. We test our packetization technique on both wavelet coding and DCT coding. Compared to other packetization techniques, we are able to achieve the same or better mean quality of the reconstructed images but with less fluctuation in quality, which is important for the viewer experience. In this way, we significantly increase the worst case quality, especially for high packet loss rates. This leads to visually more pleasing images in case of a passive reconstruction.

In this paper, we introduce the idea of using motion estimation resources from a video codec for video denoising. This is not straightforward because the motion estimators aimed for video compression and coding, tolerate errors in the estimated motion field and hence are not directly applicable to video denoising. To solve this problem, we propose a novel motion field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising. We illustrate the use of the proposed motion estimation method within a wavelet-based video denoising scheme. The resulting video denoising method is of low-complexity and receives comparable results with respect to the latest video denoising methods.

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Distributed video (DV) coding is a new paradigm that aims to achieve this. To allocate a proper number of bits to each frame, most DV coding algorithms use a feedback channel (FBC). However, in some cases, a FBC does not exist. In this paper, we therefore propose a rate allocation (RA) algorithm for pixel-domain distributed video coders without FBC. Our algorithm estimates at the encoder the number of bits for every frame without significantly increasing the encoder complexity. Experimental results show that our RA algorithm delivers satisfactory estimates of the adequate encoding rate, especially for sequences with little motion.

Limited depth of field is an important problem in microscopy imaging. 3D objects are often thicker than the depth of field of the microscope, which means that it is optically impossible to make one single sharp image of them. Instead, different images in which each time a different area of the object is in focus have to be fused together. In this work, we propose a curvelet-based image fusion method that is frequency-adaptive. Because of the high directional sensitivity of the curvelet transform (and consequentially, its extreme sparseness), the average performance gain of the new method over state-of-the-art methods is high.

We develop a new vector-based shrinkage rule, based on the concept of "signal of interest", for the removal of correlated noise. The multivariate Bessel K Form density is used for modeling the spatial correlations between complex wavelet coefficients. The interscale dependencies between the coefficients are captured using a Hidden Markov Tree model. The combined spatial and interscale model gives improvements over recently proposed Hidden Markov Models for white noise. The results show that correlated noise is suppressed well while image details are being preserved.

This paper presents a novel passive error concealment method for wavelet coded images. The proposed method is a locally adaptive directional interpolation approach, where the interpolation weights are estimated based on the available local context. For each lost low frequency coefficient, we estimate the optimal interpolation weights based on the errors that would arise by horizontally and vertically interpolating the available neighbors of the lost coefficient. Compared to older methods of similar complexity, the proposed scheme estimates the lost coefficients much better: on average, the PSNR is increased with up to 0.6 dB. The results also indicate improvements over the best available state-of-the-art techniques. The reconstructed images also look better. As our method is fast and of low complexity, it is widely usable.

In packet switched networks such as the Internet, packets may get lost during transmission due to, e.g., network congestion. This leads to a quality degradation of the original signal. As video communication is a bandwidth consuming application, the original data are first compressed. This compression step increases the impact of information loss even more. In wavelet based image and video coding, the low frequency data is the most important. Loss of low frequency coefficients results in annoying black holes in the received images and video. This effect can be countered by post processing error concealment: a lost coefficient is estimated from its neighboring coefficients. In this paper we present a locally adaptive interpolation method for the lost low frequency coefficients. For each lost low frequency coefficient, we estimate the optimal interpolation direction (horizontal or vertical) using novel error measures. In this way, we preserve the edges in the reconstructed image much better. Compared to older techniques of similar complexity, our scheme reconstructs images with the same or better quality. This is reflected in the visual as well as in the numerical results: there is an increase of up to 4.4 dB compared to bilinear concealment. The proposed scheme is fast and simple, which makes it suitable for real-time applications.

We propose an efficient and accurate wavelet based noise estimation method for white Gaussian noise in video sequences. The proposed method analyzes the distribution of spatial and temporal gradients in the video sequence in order to estimate the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the errors due to the spatio-temporal image sequence content, by a novel correction function. The main application of the proposed algorithm is for the estimation of the stationary Gaussian noise in wavelet based video processing, for which we show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to varying spatio-temporal content and noise level. Furthermore, we adapt the algorithm for local noise estimation and test its performance.

This paper reports an information-theoretic analysis of the inter-scale, inter-orientation and inter-location dependencies that exist between curvelet coefficients. We show that the marginal statistics of these coefficients can be accurately modeled using generalized Gaussian density functions. Though generally decorrelated, we find that curvelets exhibit unusually high dependencies in intra-band local micro-neighborhoods, of a magnitude not found for instance in classical wavelets. Furthermore, dependencies are subject to and decrease with increasing orientation and location differences. Finally, we conclude that intra-band coefficient dependencies are stronger than either their inter-scale or inter-direction counterparts.

We develop a low-complexity overcomplete wavelet domain method for denoising digital images corrupted with non-stationary white additive Gaussian noise. The noise level for each pixel is estimated from a local window around that pixel. We use a shrinkage function that adapts itself to the noise level and to the spatially changing statistics of the image. Experiments show that this noise model has good results for different non-stationary noise sources. Finally, we extend our method for denoising images corrupted with signal-dependent noise.

We propose a novel, Bayesian formulation of the edge-stopping (diffusivity) function in a nonlinear diffusion scheme in terms of edge probability under a marginal prior on noise-free gradient. This formulation differs from the existing probabilistic diffusion approaches that give stochastic formulations for the conductivity but not for the diffusivity function of the gradient. In particular, we impose a Laplacian prior for the ideal gradient, but the proposed formulation is general and can be used with other marginal distributions. We also make links to related works that treat correspondences between nonlinear diffusion and wavelet shrinkage.

We propose an efficient and accurate wavelet-based noise estimation method for white Gaussian noise in video sequences. The proposed method analyzes the distribution of spatial and temporal gradients in the video sequence in order to estimate the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the errors due to the spatio-temporal image sequence content, by a novel correction function. The spatial and temporal gradients are determined from the finest scale of the spatial and temporal wavelet transform, respectively. The main application of the noise estimation algorithm is in wavelet-based video processing. The results show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to varying spatio-temporal content and noise level.

This paper proposes a novel video denoising method based on nondecimated wavelet band filtering. In the proposed method, motion estimation and adaptive recursive temporal filtering are performed in a closed loop, followed by an intra-frame spatially adaptive filter. All processing occurs in the wavelet domain. The paper introduces new wavelet-based motion reliability measures. We make a difference between motion reliability per orientation and reliability per wavelet band. These two reliability measures are employed in different stages of the proposed denoising scheme. The reliability per orientation (horizontal and vertical) measure is used in the proposed motion estimation scheme while the reliability of the estimated motion vectors (MVs) per wavelet band is utilized for subsequent adaptive temporal and spatial filtering. We propose a novel cost function for motion estimation which takes into account the spatial orientation of image structures and their motion matching values. Our motion estimation approach is a novel wavelet-domain three-step scheme, where the refinement of MVs in each step is determined based on the proposed motion reliabilities per orientation. The temporal filtering is performed separately in each wavelet band along the estimated motion trajectory and the parameters of the temporal filter depend on the motion reliabilities per wavelet band. The final spatial filtering step employs an adaptive smoothing of wavelet coefficients that yields a stronger filtering at the positions where the temporal filter was less effective. The results on various grayscale sequences demonstrate that the proposed filter outperforms several state-of-the-art filters visually (as judged by a small test panel) as well as in terms of peak signal-to-noise ratio

We develop three novel wavelet domain denoising methods for subband-adaptive, spatially-adaptive and multivalued image denoising. The core of our approach is the estimation of the probability that a given coefficient contains a significant noise-free component, which we call "signal of interest." In this respect, we analyze cases where the probability of signal presence is 1) fixed per subband, 2) conditioned on a local spatial context, and 3) conditioned on information from multiple image bands. All the probabilities are estimated assuming a generalized Laplacian prior for noise-free subband data and additive white Gaussian noise. The results demonstrate that the new subband-adaptive shrinkage function outperforms Bayesian thresholding approaches in terms of mean-squared error. The spatially adaptive version of the proposed method yields better results than the existing spatially adaptive ones of similar and higher complexity. The performance on color and on multispectral images is superior with respect to recent multiband wavelet thresholding.

We develop a new filter which combines spatially adaptive noise filtering in the wavelet domain and temporal filtering in the signal domain. For spatial filtering, we propose a new wavelet shrinkage method, which estimates how probable it is that a wavelet coefficient represents a "signal of interest" given its value, given the locally averaged coefficient magnitude and given the global subband statistics. The temporal filter combines a motion detector and recursive time-averaging. The results show that this combination outperforms single resolution spatio-temporal filters in terms of quantitative performance measures as well as in terms of visual quality. Even though our current implementation of the new filter does not allow real-time processing, we believe that its optimized software implementation could be used for real- or near real-time filtering.

In this paper, we propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.

This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The spatial clustering properties are expressed in a prior model. The statistical properties concerning coefficient magnitudes and their evolution across scales are expressed in a joint conditional model. The three main novelties with respect to related approaches are (1) the interscale-ratios of wavelet coefficients are statistically characterized and different local criteria for distinguishing useful coefficients from noise are evaluated, (2) a joint conditional model is introduced, and (3) a novel anisotropic Markov random field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.

This paper describes a novel global-to-local method for the adaptive enhancement and unsupervised segmentation of brain tissues in MRI (magnetic resonance imaging) images. Three brain tissues are of interest: CSF (cerebrospinal fluid), GM (gray matter), WM (white matter). Firstly, we de-noise the image using wavelet thresholding, and segment the image with minimum error thresholding. Both the thresholdings are global-wise. Subsequently, we combine locally adaptive weighted median and weighted average filters with FCM (fuzzy C-means) clustering to achieve a local-wise segmentation. The performance of the proposed method is quantitatively validated by four indices with respect to a MRI brain phantom.

We propose an efficient and fast wavelet based technique for speckle removal from SAR images. It relies on realistic distributions of the wavelet coefficients which represent mainly speckle noise on the one hand and those that represent the useful signal corrupted by speckle on the other. We propose analytic models for these distributions, and compute their parameters automatically from a given SAR image. The resulting algorithm strongly suppresses speckle, while preserving image details and sharpness.

The visual interpretation of ultrasound brain images is a proven method to detect the white matter damage at an early stage. A problem, common to all medical ultrasound images, is the presence of speckle noise, which not only complicates the visual interpretation of images, but also quantitative measurements. This paper proposes a new filter that removes a significant amount of speckle noise from ultrasound images, while preserving details very well. The filter is based on a new cleaning technique that operates on the detail images of a wavelet decomposition. The paper illustrates that the proposed technique has some advantages over other popular techniques, i.e., the ones proposed by Lee (1980), Frost (1982), Malfait and Roose (1997), and Sattar et al. (1997).

We propose a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov random field model. An iterative updating technique known as iterated conditional modes (ICM) is applied to estimate the binary masks containing the positions of those wavelet coefficients that represent the useful signal in each subband. For each wavelet coefficient a shrinkage factor is determined, depending on its magnitude and on the local spatial neighbourhood in the estimated mask. We derive analytically a closed form expression for this shrinkage factor.