ControlNet has significantly advanced controllable image generation by integrating dense conditions (such as depth and canny edges) with text-to-image diffusion models. However, ControlNet's integration requires an additional amount nearly equal to half of the base diffusion model's parameters, making it inefficient. To address this, we introduce Simple-ControlNet, an efficient and streamlined network for controllable text-to-image generation. It employs a single-scale projection layer to incorporate condition information into the denoising U-Net. It is supplemented by Low-Rank Adapter (LoRA) parameters to facilitate condition learning. Impressively, Simple-ControlNet requires fewer than 3 million parameters for the control mechanism, substantially less than the 300 million needed by ControlNet. Our extensive experiments confirm that Simple-ControlNet matches and surpasses ControlNet's performance across a broad range of tasks and base diffusion models, showcasing its utility and efficiency.

Ordered statistics decoding (OSD) requires Gaussian elimination (GE) to obtain the systematic generator matrix (SGM) for yielding codeword candidates, resulting in an uncompromised latency. Recently, low-latency OSD (LLOSD) has been proposed for BCH codes to avoid GE by computing the SGM of a Reed-Solomon (RS) code. Since BCH codes are binary subcodes of the RS codes, the BCH codeword candidates can be yielded with the RS SGM. To further facilitate the LLOSD, this paper proposes a local constraint-based LLOSD (LC-LLOSD). In particular, the RS SGM is converted into a binary BCH parity-check matrix, whose submatrix can be used to specify a trellis. The serial list Viterbi algorithm (SLVA) can be applied to generate extended test messages (TMs). Further, it can be facilitated by incorporating the TM generation scheme of the LLOSD. Since the SLVA generates the TMs in decreasing likelihood, the LCLLOSD can yield better decoding performance while re-encoding far less TMs compared to the LLOSD. Simulation results show the LC-LLOSD's complexity advantage over the LLOSD.

Medical image segmentation often suffers from the limitation of small-scale annotated datasets. To address this challenge, we propose Paired Diffusion Models (PDM), a framework that adapts large-scale pre-trained diffusion models (e.g., Stable Diffusion) to generate paired image-label data. By modifying the neural network’s architecture to accept and output both images and their pixel-level annotations simultaneously, PDM enables a single model to generate an image and its corresponding binary segmentation mask. Furthermore, we introduce a mask decoder that directly decodes binary masks from the latent space, bypassing the need for an intermediate RGB-to-binary conversion step. By carefully initializing additional parameters, PDM stabilizes training and enhances the accuracy of the generated segmentation masks. Experimental results demonstrate that pretraining with images generated by PDM significantly boosts the performance of medical image segmentation models, especially in scenarios with limited annotated data.

We introduced StyleEntity, a zero-shot image manipulation model that utilizes named entities as proxies during its training phase. This strategy enables our model to manipulate images using unseen textual descriptions during inference, all within a single training phase. Additionally, we proposed an inference technique termed Prompt Ensemble Latent Averaging (PELA). PELA averages the manipulation directions derived from various named entities during inference, effectively eliminating the noise directions, thus achieving stable manipulation. In our experiments, StyleEntity exhibited superior performance in a zero-shot setting compared to other methods. The code, model weights, and datasets are available at https://github.com/feng-zhida/StyleEntity.

Segmenting intricate curve structures like retinal blood vessels, encompassing both fine and coarse details, remains a significant challenge. This work proposes a novel module that divides these complex curve structures into trunks and branches, fuses the input as auxiliary information, and optimizes the breakpoints of different curve parts through losses. In order to balance the redundant information that may lead to model overfitting, a unique feature perturbation strategy is introduced after the backbone decoding process to enhance the model’s robustness to complex curve structure segmentation tasks. Experiments show that this method can effectively distinguish different topological structures of blood vessels and maintain high segmentation accuracy even at blood vessel intersections or breakpoints, which holds immense potential for diverse future applications in image segmentation.

In the segmentation of curve structures, the discontinuity may lead to an incomplete topological representation of the structure. The current methods for reconnecting curve structures lack physical explanations and are limited in their effectiveness in image processing. In order to address these constraints, a new algorithm for reconnecting curve structures in segmentation is proposed by combining fluid mechanics principles, especially Bernoulli’s principle and random walks. This algorithm calculates the similarity of fracture curves to find the fracture curve.Redefined the energy calculation method during the reconnection process and calculated the probability of energy transfer. It adopts a biased random walk guided by the energy transfer probability in the graph until the walker reaches the target. This innovative approach provides a more comprehensive physical explanation and improves the effectiveness of image processing.

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.01not only achieves a new state-of-the-art on MS-COCO with zero-shot FID-30k score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.

Abstract Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multi-scale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method outperforms the state-of-the-art methods in accuracy, sensitivity, specificity and also area under receiver operating characteristic curve.

A key challenge of saliency computation in foggy images is how to effectively detect salient objects which are less visible. The primary cause may lie in the fact that the light scattering through fog particles reduces image contrast. In this paper, we propose a frequency-spatial fusion saliency computational model based on discrete stationary wavelet transform (DSWT). The input image is firstly transformed into HSV color space, and the amplitude spectrum of each color channel is adjusted to generate the frequency domain saliency map. Then, the local-global superpixel contrast is measured to obtain the spatial domain saliency map. The DSWT is finally utilized to fuse the frequency-spatial cues. Experimental results indicate that the proposed model can efficiently reduce the influence of light scattering through fog particles, and can achieve the best performance in foggy images comparing to 16 state-of-the-art saliency models.

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Optical coherence tomography (OCT) is one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is very crucial for doctors to diagnose and study retinal diseases. However, manual segmentation is often a time-consuming and subjective process. A number of methods have been proposed for layer segmentation on retinal OCT images, but these methods are not suit for retinal pathological OCT images. In this work, we propose a new method for layers (two layers, inner limiting membrane, outer segments-retinal pigment epithelium) segmentation in pathological retinal OCT images using shortest path algorithm enhanced with backtracking and direction consistency. To quantitate the performance of the proposed method, we compared method to three segmentation methods. The experimental result shows that our method is more suited for retinal OCT images in pathological and achieves better result than the state-of-the-art methods.

This paper studies the automatic classification of abnormal mutant and normal fishes by analyzing their movements recorded in the video. Motivated by the observation that mutant fishes have muscle disorders so that their bodies cannot bend sufficiently to swim normally, a kinematics-driven movement change detection approach is proposed to automatically segment the recorded video into different video segments. Furthermore, a new feature extraction method, called local kinematics shape pattern (LKSP), is proposed in this paper to provide discriminative spatiotemporal kinematics measurements of fish body movements. The histogram of the proposed LKSP features is incorporated into a motion classification approach to identify whether the fish is normal or a mutant. The experiments are conducted using the real-world recorded videos to demonstrate the superior performance of the proposed approach.

The objective of image contrast enhancement is to improve the contrast level of images, which are degraded during image acquisition. Image contrast enhancement is considered as an optimization problem in this paper and the artificial bee colony (ABC) algorithm is utilized to find the optimal solution for this optimization problem. The contribution of the proposed approach is two-fold. First, in view of that the fitness function is indispensable to evaluate the quality of the enhanced image, a new multiple objective fitness function is proposed in this paper. Second, the image transformation function is critical to generate new pixel intensities for the enhanced image from the original input image; more importantly, it guides the searching movements of the artificial bees. For that, a parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm. This is in contrast to that the whole space of image intensity levels is used in the conventional ABC-based image enhancement approaches. Extensive experiments are conducted to demonstrate that the proposed approach outperforms conventional image contrast enhancement approaches to achieve both better visual image quality and higher objective performance measures.

Biomedical studies show that mutant genes in transgenic mutant fishes can lead to muscle disorders so that their swimming capabilities are affected. This paper studies the automatic detection of abnormal mutant fishes by analyzing their movements in the video. To differentiate between normal fish and mutant fish, a new feature extraction method, called spatiotemporal local kinematics pattern (STLKP), is proposed in this paper to provide discriminative spatiotemporal kinematics measurements of fish body movements. Furthermore, the histogram of the proposed STLKP features is incorporated into a motion classification approach to identify whether the fish is normal or a mutant. A large collection of real-world recorded videos is used in experiments to demonstrate that the proposed approach outperforms the conventional approaches to provide more accurate abnormal motion detection performance.

Blind noisy assessment aims to evaluate the quality of the degraded noisy image/video without the need for the ground-truth image. To tackle this challenge, this paper proposes a new blind noise assessment approach based on the path information of the ants' movement. The proposed modified ant shortest path (MASP) algorithm uses the path information of the ant colony optimization (ACO). The contribution of the proposed approach is two-fold. First, the proposed approach utilizes a number of artificial ants to move on a 2-D graph for constructing the path information, and calculates the ants' movement path driven by the shortest path strategy. Second, several path statistics metrics are proposed to evaluate the image quality. Experimental results are provided to demonstrate that the proposed image quality assessment approach is effective for both benchmark image database and real-world noisy video.

Multimedia data is usually represented with different low-level features, and different types of multimedia data, namely multimodal data, often coexist in many data sources. It is interesting and challenging to learn comprehensive semantics from multiple low-level features for multimodal data analysis. In this paper, we propose a new algorithm, namely hierarchical multi-feature fusion for multimodal data semantics understanding. Our approach explores intra-modality structural information derived from each type of feature, and further proposes transductive inter-modality fusion strategy, which analyzes canonical correlation between different modalities. Extensive experiments are conducted on collected multimodal database for data classification application. The experiment results show that the performance of our algorithm is remarkable and demonstrate its superiority over several existing algorithms.

Visual saliency is an important cue in human visual system, it can identify salient region in image. Image contrast has been utilized as an effective feature to detect the salient region. The conventional contrast measures utilize both spectral and spatial properties of image in many salient region detection methods. However, they only consider the local characteristics of image region, consequently, the global characteristics are neglected. This paper presented a new contrast measure by exploiting both local and global characteristics of image regions. Furthermore, the proposed measure is utilized to perform salient region detection in image. Experiments are conducted on the MSRA test database to compare the performance of the proposed approach with the state-of-the-art salient region detection algorithms.

This paper presents a novel despeckling algorithm that can be used to enhance image quality in medical ultrasound images. Firstly, the log-transformed images are transformed by dual-tree complex wavelet transform (DTCWT). And then, we use a non-Gaussian statistical model with an adaptive smoothing parameter for ideal image signal in the transformed domain. According to Bayesian theory, the MAP estimator is obtained with a proposed adaptive threshold which has better despeckling performance by exploiting the interscale properties of wavelet coefficients. The proposed approach results in significant speckle reduction and preserve details of ultrasound images at the same time while the introduced distortions are not noticeable.

Multimodal data is more and more popular in recent years. It is most interesting and challenging to learn multimodal data representation which affects the performance of relevant applications greatly, such as retrieval and clustering. However, it is difficult to find an efficient representation for multimedia data of different modalities which are heterogeneous in low-level features. Also it is hard to bridge the semantic gap between features and semantics. In this paper, we propose an isomorphic and sparse multimodal data representation method. First, we learn an isomorphic content representation by analyzing kernel canonical correlation among heterogeneous features; secondly, we propose optimization strategy of graph-based semantic sparse boosting. Extensive experiments demonstrate the superiority of our method over several existing algorithms.

Blind noisy image quality assessment aims to evaluate the quality of the degraded noisy image without the need for the ground truth image. To tackle this challenge, this paper proposes an image quality assessment approach using block homogeneity. The contribution of the proposed approach is two-fold. First, a block-based homogeneity measure is proposed to estimate the statistics (e.g., variance) of the noise incurred in the image, based on adaptively selected homogeneous image regions. Second, an image quality assessment approach is proposed by exploiting the above-mentioned estimated noise variance, along with the visual masking effect of the human visual system. Experimental results are provided to demonstrate that the proposed image noise estimation approach yields superior accuracy and stability performance to that of conventional approaches, and the proposed image quality assessment approach achieves consistent performance to that of human subjective evaluation.

The estimation of noise statistics is critical to optimize many computer vision algorithms. The main issue is how to identify the homogeneous image patches for estimating the noise statistics. The smallest variance used in the conventional approaches is not always a good measure of homogeneity of image patches. In addition, the conventional approaches neglect the fact that homogeneous image patches tend to cluster together due to local spatial smoothness in images. In view of this, a new image noise estimation approach is proposed in this letter. The proposed approach has two key components. First, a graphical representation is proposed to model the relationship among image patches. Second, the ant colony optimization (ACO) technique is used to automatically select a set of patches for estimating the noise statistics. To be more specific, the proposed approach guides the spatial movement of artificial ants towards homogeneous locations in the graph, by considering both global (i.e., clustering measure) properties and local (i.e., homogeneity measure) properties of patches. Experimental results are provided to justify that the proposed approach out-performs nine conventional approaches to provide more accurate noise statistics estimation.

This paper presents a novel algorithm for vascular tree segmentation based on shortest path connection. The connected vascular tree provides topological features that are instrumental in image-aided diagnosis. The proposed method can enforce the connectivity as well as remove the false detection at same time. Multi-scale ridge detector is employed that can locate vessels with different widths. To connect the isolated ridge, the single-source shortest path algorithm is tailored that searches the optimal path. The path metric is defined in terms of probability of pixel belong to foreground and background. This mechanism enables that the false detection could be removed via hypothesis testing. The topological vascular tree with 1-pixel width and fully-connection is segmented from the retinal image. The simplicity and efficiency of the proposed method make it practical to be employed in image-aided diagnosis system readily.

This paper presents a new structural feature for feature-based retinal image registration. The conventional point-matching methods largely depend on the branching angles of single bifurcation point. The feature correspondence across two images may not be unique due to the similar angle values. In view of this, structure-matching registration is favored. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. This can greatly reduce the ill-posed nature of the matching process as long as the vasculature pattern can be segmented. The simplicity and efficiency of the proposed method make it readily to be applied alone or incorporated with other existing methods to formulate a hybrid or hierarchy scheme.

Resolution enhancement for stereo imaging aims to use a pair of stereo images to reconstruct another pair of images with higher resolution. To tackle this problem, a Bayesian resolution enhancement approach is proposed in this paper. Since the prior image model is essential for solving the ill-posed numerical issues encountered in the image resolution enhancement, the proposed approach exploits a prior image model which considers both the spatial local smoothness constraint within each reconstructed high-resolution image and the disparity-compensated local smoothness constraint between the pair of reconstructed high-resolution images. Then the proposed prior image model is further incorporated into a Bayesian inference formulation to perform stochastic image reconstruction. Experiments are conducted to demonstrate the superior performance of the proposed approach.

This paper proposes a new image super-resolution (SR) approach to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images with zooming motion. Most conventional SR image reconstruction methods assume that the motion among different images consists of only translation and possibly rotation. This in-plane motion model, however, is not practical in some applications, when relative zooming exists among the acquired LR images. In view of this, this paper presents a new SR method that addresses a motion model including both in-plane motion (e.g. translation and rotation) and zooming motion. Based on this model, a maximum a posteriori (MAP) based SR algorithm using L1-norm optimization is proposed. Experimental results show that the proposed algorithm based on the new motion model performs well in terms of visual evaluation and quantitative measurement.

Particle filter is a popular stochastic tracker for object tracking. In articulated human body pose tracking, lots of work focuses on increasing sampling efficiency by incorporating optimization algorithm into particle filter. In this study, we propose a modified optimization based particle filter algorithm for pose tracking. The new algorithm can maintain the diversity of particle set by using a suppression scheme. Experimental results show that the proposed method can cope with multi-modality and can obtain more accurate estimation than other optimization based particle filter methods.

The aim of multi-focus image fusion is to combine multiple images with different focuses for enhancing the perception of a scene. The critical issue in the design of multi-focus image fusion algorithms is to evaluate the local content information of the input images. Motivated by the observation that the marginal distribution of the wavelet coefficients is different for images with different focus levels, an image fusion approach using wavelet-domain statistics is proposed in this paper. The proposed approach exploits the spreading of the wavelet coefficients distribution to measure the degree of the image's blur. Furthermore, the wavelet coefficients distribution is evaluated using a locally-adaptive Laplacian mixture model. Extensive experiments are conducted using three sets of test images under three objective metrics to demonstrate the superior performance of the proposed approach.

This paper proposes a new regularization scheme to address blind color image deconvolution. Conventional blind monochromatic image deconvolution algorithms handle each color channel independently, thereby ignoring the inter-channel correlation present in the color images. Further, most existing blind color deconvolution algorithms do not take the parametric information of the blurs into consideration. In view of these, a regularization scheme is proposed to perform blind color image deconvolution. A new regularization operator is developed in the blur domain. A reinforcement blur modeling scheme is adopted to evaluate the relevance of manifold parametric blur structures, and the information is integrated into the deconvolution scheme. In addition, a regularization scheme for image is developed to recover edges of color images and reduce color artifacts. Experimental results show that the method is able to achieve satisfactory restored color images under noisy environment.

This paper proposes an effective higher order statistics method to address subpixel image registration. Conventional power spectrum-based techniques employ second-order statistics to estimate subpixel translation between two images. They are, however, susceptible to noise, thereby leading to significant performance deterioration in low signal-to-noise ratio environments or in the presence of cross-correlated channel noise. In view of this, we propose a bispectrum-based approach to alleviate this difficulty. The new method utilizes the characteristics of bispectrum to suppress Gaussian noise. It develops a phase relationship between the image pair and estimates the subpixel translation by solving a set of nonlinear equations. Experimental results show that the proposed technique provides performance improvement over conventional power-spectrum-based methods under different noise levels and conditions.

This paper presents a new algorithm for demosaicking images captured through a color filter array (CFA). The objective of the CFA demosaicking is to render a full color image from the mosaicked image. This is commonly achieved by estimating the missing color information from the surrounding observed pixels. In this paper, we integrate the observation that color images have a strong intrachannel spatial correlation in low-frequency components and a dominant interchannel correlation in the high- frequency components. A new framework is proposed to utilize this information where the missing pixels in each color channel are estimated from the wavelet subbands. A modified median-filtering operation is then applied in the subband domain. The algorithm is adaptive and produces superior full-resolution images when compared with other methods.

This paper presents a new hybrid model framework to address blind color image deconvolution. Blind color image deconvolution is a challenging problem due to the limited information on the blurring function. Conventional methods based on the single-input single-output (SISO) model experience suboptimal results as each color channel is processed independently. On the other hand, there are limitations on the practicality of using a multiinput multioutput (MIMO) model in solving this problem as the color channels are usually highly correlated. In view of these constraints, this paper proposes a novel framework to solve blind color image deconvolution by first decomposing the color channels into wavelet subbands, and performing image deconvolution using a hybrid of SISO and single-input multioutput models. The proposed method utilizes the correlation information among different color channels to alleviate the constraints imposed by the MIMO systems. Experimental results show that the method is able to achieve satisfactory restored images under different noise and blurring environments.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR) by fusing multiple blurred low-resolution (LR) images to render a high-resolution (HR) image. Conventional super-resolution (SR) image reconstruction algorithms assume either the estimated motion (displacement) errors by existing registration methods are negligible or the displacement is known a priori. This assumption, however, is impractical as the performance of existing registration algorithms is still less than perfect. In view of this, we present a new estimation framework that performs joint image registration and HR reconstruction. An iterative scheme based on nonlinear least squares method is developed to estimate the motion shift (displacement) and HR image progressively. The motion model that is considered in this work includes both translation as well as rotation. Experimental results show that the proposed method is effective in performing image super-resolution.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.

This paper proposes an effective higher-order statistics method to address subpixel image registration. Conventional power spectrum-based techniques employ second-order statistics to estimate subpixel translation between two images. They are, however, susceptible to noise, thereby leading to significant performance deterioration in low signal-to-noise (SNR) environments. In view of this, we propose a bispectrum-based approach to alleviate this difficulty. The new method utilizes the characteristics of bispectrum to suppress Gaussian noise. It develops the phase relationship between the image pair, and estimates the subpixel translation by solving a set of nonlinear equations derived from the bispectrum. Experimental results show that the proposed method is effective in identifying subpixel translations under different noise levels and environments.

This paper proposes a new algorithm to address blind image super-resolution by fusing multiple blurred low-resolution (LR) images to render a high-resolution (HR) image. Conventional super-resolution (SR) image reconstruction algorithms assume either the blurring during the image formation process is negligible or the blurring function is known a priori. This assumption, however, is impractical as it is difficult to eliminate blurring completely in some applications or characterize the blurring function fully. In view of this, we present a new maximum a posteriori (MAP) estimation framework that performs joint blur identification and HR image reconstruction. An iterative scheme based on alternating minimization is developed to estimate the blur and HR image progressively. A blur prior that incorporates the soft parametric blur information and smoothness constraint is introduced in the proposed method. Experimental results show that the new method is effective in performing blind SR image reconstruction where there is limited information about the blurring function.

In this paper, we present a new and efficient algorithm for image interpolation. To render high-resolution image from low-resolution image, classical interpolation techniques estimate the missing pixels from the surrounding pixels based on pixel-by-pixel basis. In contrast, this paper proposes an algorithm which is centered on Tikhonov regularization. The regularized solution is derived using the framework of damped least square optimization. Kronecker product and singular value decomposition are employed to reduce the computational cost of the algorithm. Experimental results show that the method produces better interpolation results when compared to other conventional techniques.

In this paper, we present a new and efficient demosaicking algorithm for color filter array (CFA). To render a full-resolution color image using a single-chip camera, the missing color information must be estimated from the surrounding pixels. We take advantage of the observation that the color image has strong inter-channel correlation in the high-frequency subbands. For each color channel, the missing colors are synthesized from the wavelet subbands. The low-frequency subband is estimated by conventional interpolation techniques using intra-channel data. The high-frequency subbands are estimated from the inter-channel data with consideration of the CFA pattern. The algorithm is adaptive in nature and produce superior full-resolution image when compared to other demosaicking techniques.

This paper presents a new framework to address blind color image deconvolution based on wavelet decomposition. Blind color image deconvolution is a challenging problem due to the lack of information available. Conventional methods based on single-input single-output (SISO) model experience significant color artifacts in the restored images. On the other hand, there are limitations on the practicality of using multi-input multi-output (MIMO) model in solving this problem as the color channels are usually highly correlated. In view of this, the paper proposes a new framework to solve blind color image deconvolution by first decomposing the color channels into wavelet subbands, and performing image deconvolution using a combination of SISO and single-input multi-output (SIMO) models. Experimental results show that the proposed method is able to achieve satisfactory restored images.

This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.

This paper proposes an adaptive blind image deconvolution scheme based on fuzzy K-nearest-neighbor (FKNN) algorithm. It is well known that most point-spread functions (PSFs) satisfy up to a certain degree of parametric structure. The method incorporates such knowledge about the PSF structure by estimating the PSF according to its K nearest neighbors. Through a process of neighbor generation, model matching, and fuzzy weighted mean filtering, FKNN provides a robust estimate for the blur. This further improves the convergence performance in blind deconvolution process. Experimental results show that it is effective in restoring degraded images where there is little prior knowledge about the blur.