Dose planning is essential in radiotherapy for cancer patients, yet current practice relies on iterative manual optimization, underscoring the need for automated prediction. Existing deep learning approaches remain limited because they often ignore the 3D spatial relationships between tumors and surrounding organs at risk (OARs), and clinical priors on safe dose thresholds. To overcome these limitations, we propose DKVMU-Net, a distance-aware and knowledge-driven Vision Mamba U-Net for automated dose prediction. Our framework incorporates Vision Mamba blocks to capture global, long-range dependencies from CT scans and OAR signed distance field (SDF) maps, which naturally encode spatial information. Additionally, we introduce a deformable dynamic feature enhancement module (DDFEM) for texture refinement, followed by a linear crossattention fusion module to improve cross-modality integration. A customized loss function is also designed to incorporate prior knowledge of OAR dose constraints, ensuring optimal target coverage and OAR protection. To alleviate the scarcity of doseplanning datasets, we collect an in-house radiotherapy lung cancer dataset (RLCD), consisting of CT volumes, OAR masks, and corresponding SDF maps from 116 patients. We evaluate our DKVMU-Net on both the in-house dataset and public available OpenKBP dataset. Compared with the sate-of-the-art method, our approach achieves an 11.6 % improvement in dose score (1.641 vs. 1.857) and 26.3 % in DVH score (6.481 vs. 8.799) on RLCD, and a 7.8 % improvement in dose score (2.421 vs. 2.626) and 13.9 % in DVH score (1.057 vs. 1.227) on OpenKBP. These results demonstrate the robustness and effectiveness of our approach.

Deformable image registration is essential in medical applications, as accurately estimating organ displacements across respiratory phases enables precise radiation dose planning in dynamic environments, mitigates damage to organs at risk (OARs), and thus improves patients' health-related quality of life. Although current learning-based methods have achieved impressive performance in small deformation registration, challenges remain due to their limited ability to capture large deformations occurring during respiration. To address this issue, we propose a novel Mamba-based hierarchical registration framework that effectively extracts both global and local features for accurate deformation prediction. Specifically, given a pair of source and target 3DCT volumes, we incorporate a foundation model pretrained on medical image registration tasks to enhance alignment accuracy. We further propose a directional-deformable Mamba scheme to facilitate global context extraction and local motion awareness. The directional Mamba component scans input features from multiple orientations to achieve broad contextual perception, while the deformable Mamba module employs adaptive directional scanning strategies to capture dynamic local variations. To overcome the scarcity of annotated respiratory data, we also collect a new respiratory lung cancer dataset comprising 100 annotated phases from 20 patients. Experimental results on our in-house dataset demonstrate that our method outperforms state-of-the-art approaches, achieving a 1.3 % improvement in overall Dice accuracy and a 1.6 dB increase in PSNR, underscoring its strong potential for clinical deployment. Code and test data are available at: https://github.com/yangyangshi806/Mamba_based_Registration.

This work addresses a significant gap in existing image and video databases commonly used in computer vision applications by introducing a unique and comprehensive database named Photo-realistic Multi-Distortion Video Dataset for Benchmarking and Developing Robust Computer Vision Models (PVD4RCV). A key innovation of PVD4RCV lies in its incorporation of some relevant physical factors (e.g. depth information, interaction of light with scene contents) inherent to video signal acquisition in constrained and complex real-world environments, which are used to generate realistic distortions in video sequences (e.g. local motion blur, local defocus blur). PVD4RCV includes a diverse collection of videos featuring common distortions, real-world scenarios, and contextual variations. It includes both original and degraded video versions, along with detailed annotations to support the development of advanced learning models, particularly for tasks such as distortion classification and object detection. This resource aims to advance research and applications in computer vision by providing a robust foundation for model training and evaluation. The database is open source and available at the following link for the community: https://github.com/Aymanbegh/PVD4RCV

User-generated content (UGC) videos are in constant increase, a direct consequence of the number of devices equipped with cameras. Millions of videos are captured and shared across various platforms. These videos often suffer from numerous distortions, among which shakiness is one of the most prominent and visually uncomfortable. The development of quality metrics is needed, both for simple quality assessment or for post-treatment purposes, such as evaluating video stabilization algorithms. In this paper, we propose a new no-reference metric named I3D_shake, based on Inflated 3D ConvNets architecture (I3D). This study investigates the impact of input characteristics, specifically frame type and frame count. Comprehensive experiments are conducted and compared to state-of-the-art metrics. The codes of the I3D_shake are available at https://github.com/dborhen/I3D_shake.git.

This research presents a comprehensive investigation into subjective video shakiness assessment. A collection of 30 shaky videos was gathered, covering relevant categories such as climbing, driving, large parallax, rotation, running, and walking, with different scenes and levels of shakiness. A pairwise comparison (PWC) was conducted, involving human observers who evaluated the perceived quality of shaky videos, and the results have been converted into quality scores using the Just-Objectionable-Differences (JOD) scaling method. The shakiness assessment framework was proved effective by correlations between objective metrics and subjective judgments, and it can serve as a benchmark for future advancements in the field, fostering improvements in video stabilization technologies and applications. The complete dataset is made publicly available through the following link:Shakiness-QuAD

In this paper, we introduced a novel Facial Emotion Recognition (FER) framework that utilizes a diffusion-based approach and an attention mechanism. The model is efficiently trained through self-supervised learning, leveraging labeled and unlabelled data. The proposed framework has been rigorously tested on the FER2013 and AffectNet datasets, achieving promising accuracies of $67.2 \%$ and $68.1 \%$, respectively. The quantitative results not only surpass the performance of existing state-of-the-art FER models but also demonstrate the synergistic effect of combining diffusion-based modeling with self-supervised learning and attention mechanisms within a solid architectural framework. Our approach sets a new benchmark in the field, offering a significant step forward in the accurate and efficient recognition of facial expressions.

Cameras are now available at extremely low prices due to ongoing advancements in image acquisition hardware. However, the quality of images can be compromised by various distortions that occur throughout the entire process, from acquisition to processing and delivery. Over the past few decades, researchers have primarily focused on developing algorithms to assess the quality of distorted images. Unfortunately, certain distortions can also result from enhancement processes, such as over-enhancement and color saturation. Although there are metrics available for measuring contrast levels in images, there is currently no standard metric for evaluating the extent and effects of contrast enhancement. In this paper, we propose a new framework that expands the evaluation of contrast levels to ranking contrast-enhanced images. Our technique involves extracting a new set of features that accurately describe the effects of contrast enhancement. Furthermore, we integrate additional statistical indicators, such as skewness and kurtosis, which describe the degree of visual satisfaction linked to human perception. These identified characteristics are subsequently use with a simple classification module to determine the rank order for a given collection of contrast enhanced images. The results show excellent accuracy in correct ranking which outperforms state-of-the-art by more than $15 \%$.

In this paper, we propose a new comprehensive Video Surveillance Quality Assessment Dataset (VSQuAD) dedicated to Video Surveillance (VS) systems. In contrast to other public datasets, this one contains many more videos with distortions and diversified content from common video surveillance scenarios. These videos have been artificially degraded with various types of distortions (single distortion or multiple distortions simultaneously) at different severity levels. In order to improve the efficiency of the surveillance systems and the versatility of the video quality assessment dataset, night vision CCTV videos are also included. Furthermore, a comprehensive analysis of the content in terms of diversity and challenging problems is also presented in this study. The interest of such database is twofold. First, it will serve for benchmarking different video distortion detection and classification algorithms. Second, it will be useful for the design of learning models for various challenging VS problems such as identification and removal of the most common distortions. The complete dataset is made publicly available as part of a challenge session in this conference through the following link: https://www.l2ti.univ-paris13.fr/VSQuad/.

A frequent degradation in video-guided surgery and especially laparoscopic and endoscopic surgery is uneven illumination, due in large part to physical limitations of the sensors and uncontrolled lighting conditions in the internal structure of the digestive tract and particularly at the level of the intestines. Surgical as well as postoperative task accuracy can be seriously affected by the perceptual quality of the acquired images or videos. In this respect, a No-Reference Image Quality Assessment (NR-IQA) metric dedicated to uneven illumination is proposed in this paper. The key idea is to analyze the effect of contrast enhancement on the spatial distribution of the luminance component of the signal. The results obtained through extensive experiments, performed on a challenging dedicated database, have shown that the proposed metric significantly improves the state-of-the-art NR-IQA metrics when applied to this type of video content and distortion.

Over past decades, many image encryption algorithms have been proposed, among which we can cite the perceptual/selective encryption methods which have attracted wide attention. Such methods allow for adjusting the scrambling intensity, it is therefore essential to have a reliable visual security metric to adjust the scrambling intensity on the one hand and to evaluate the visual security of encrypted images on the other hand. Usually, these tasks are performed based on classical randomness-based measures or image quality assessment metrics. However, these methods have shown their inadequacy as a visual security metric, as they do not address content intelligibility, which represents an essential security requirement. Moreover, these methods are either dedicated to the prediction of visual security (VS) or visual quality (VQ), but not both. In this paper, we propose a no-reference (NR) visual security metric for perceptually encrypted images based on deep multi-task learning, which we dub the Multi-Task Visual Security (MTVS) metric. The proposed metric consists of one shared convolutional neural network (CNN) followed by two separate sub-networks of fully-connected (FC) layers, where one sub-network is responsible for predicting the VS score, while the other is for predicting the VQ score. Experiments were performed on two publicly perceptually encrypted image databases and the results show that the proposed metric yields superior performance on both VS and VQ prediction tasks. The source code and models are available at: https://github.com/Mamadou-Keita/MTVS.

In this paper we propose a new method for document image restoration based on Blind Source Separation. The existing separation methods rely on the general properties of source images such as independence, sparsity, and non-negativity. In this work we show that by exploiting some characteristics of image denoising methods in a play-and-plug scheme, efficient BSS results could be achieved. In particular, we show that the use of BM3D and Non-local Means denoising methods as ingredients in the proposed scheme, which exploits the non-local properties of the image, leads to better image separation in terms of convergence rate and perceptual image quality. We also propose to use the dictionary-learning approach to take the concept of visual chirality into consideration. Finally, we apply the proposed scheme to document image restoration problem and show its advantage through experiments and objective performance evaluation.

Abstract Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies’ silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach.

In this paper, we propose a new formulation for the blind source separation problem for audio signals with convolutive mixtures to improve the separation performance of Independent Vector Analysis (IVA). The proposed method benefits from both the recently investigated convolutive approximation model and the IVA approaches that take advantages of the cross-band information to avoid permutation alignment. We first exploit the link between the IVA and the Sparse Component Analysis (SCA) methods through the structured sparsity. We then propose a new framework by combining the convolutive narrowband approximation and the Windowed-Group-Lasso (WGL). The optimisation of the model is based on the alternating optimisation approach where the convolutive kernel and the source components are jointly optimised.

An effective contrast enhancement method should not only improve the perceptual quality of an image but should also avoid adding any artifacts or affecting naturalness of images. This makes Contrast Enhancement Evaluation (CEE) a challenging task in the sense that both the improvement in image quality and unwanted side-effects need to be checked for. Currently, there is no single CEE metric that works well for all kinds of enhancement criteria. In this paper, we propose a new Multi-Criteria CEE (MCCEE) measure which combines different metrics effectively to give a single quality score. In order to fully exploit the potential of these metrics, we have further proposed to apply them on the decomposed image using wavelet transform. This new metric has been tested on two natural image contrast enhancement databases as well as on medical Computed Tomography (CT) images. The results show a substantial improvement as compared to the existing evaluation metrics. The code for the metric is available at: https://github.com/zakopz/MCCEE-Contrast-Enhancement-Metric.

In this paper, we propose an effective Convolutional Autoencoder (AE) model for Sparse Representation (SR) in the Wavelet Domain for Classification (SRWC). The proposed approach involves an autoencoder with a sparse latent layer for learning sparse codes of wavelet features. The estimated sparse codes are used for assigning classes to test samples using a residual-based probabilistic criterion. Intensive experiments carried out on various datasets revealed that the proposed method yields better classification accuracy while exhibiting a significant reduction in the number of network parameters, compared to several recent deep learning-based methods.

People counting in sports venues is emerging as a new domain in the field of video surveillance. People counting in these venues faces many key challenges, such as severe occlusions, few pixels per head, and significant variations in person's head sizes due to wide sport areas. We propose a deep model based method, which works as a head detector and takes into consideration the scale variations of heads in videos. Our method is based on the notion that head is the most visible part in the sports venues where large number of people are gathered. To cope with the problem of different scales, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the Convolutional Neural Network (CNN) and it provides a response matrix containing the presence probabilities of people observed across scene scales. We then use non-maximal suppression to get the accurate head positions. For the performance evaluation, we carry out extensive experiments on two standard datasets and compare the results with state-of-the-art (SoA) methods. The results in terms of Average Precision (AvP), Average Recall (AvR), and Average F1-Score (AvF-Score) show that our method is better than SoA methods.

A plethora of Contrast Enhancement (CE) methods has been proposed in the literature. Each of these has its own strengths and limitations. Further, the quality of the resulting enhanced images depends upon the original image and its content. Hence, a given CE method can provide good quality for a certain image but a poorer quality for another. In this paper, we propose a novel workflow to provide an automatic ranking of enhanced images which may have been obtained using different techniques. The proposed technique is based on a Convolutional Neural Network (CNN) using saliency information. The idea is to start by comparing two enhanced versions of a given image in order to select the best one automatically based on perceived quality. Here, a saliency map is used to select relevant patches which are highly correlated with the human visual system sensitivity. The well-known Structural Similarity Image Metric (SSIM) map is also employed to compare the similarity between both enhanced images. Using such information, a CNN model is trained to predict the rank in terms of the image quality as perceived by humans. The algorithm is tested over three CE benchmarking databases with the experimental results validating the superiority of the proposed system as compared to state-of-the-art CE evaluation techniques.

The success of minimally invasive interventions and the remarkable technological and medical progress have made endoscopic image enhancement a very active research field. Due to the intrinsic endoscopic domain characteristics and the surgical exercise, stereo endoscopic images may suffer from different degradations which affect its quality. Therefore, in order to provide the surgeons with a better visual feedback and improve the outcomes of possible subsequent processing steps, namely, a 3-D organ reconstruction/registration, it would be interesting to improve the stereo endoscopic image quality. To this end, we propose, in this paper, two joint enhancement methods which operate in the wavelet transform domain. More precisely, by resorting to a joint wavelet decomposition, the wavelet subbands of the right and left views are simultaneously processed to exploit the binocular vision properties. While the first proposed technique combines only the approximation subbands of both views, the second method combines all the wavelet subbands yielding an inter-view processing fully adapted to the local features of the stereo endoscopic images. Experimental results, carried out on various stereo endoscopic datasets, have demonstrated the efficiency of the proposed enhancement methods in terms of perceived visual image quality.

In the last two decades many nonlinear anisotropic diffusion-based approaches have been proposed to deal with document image enhancement. However, all these methods are based on an iterative process that highly depends on two crucial parameters K±, used to separate coefficients representing foreground edges from those representing artifacts into eigenvalues matrices λ±. These parameters are tuned manually. In this paper, a new approach which blindly and automatically highlights eigenvalues coefficients representing foreground strokes is proposed. This solution is then integrated into a full anisotropic diffusion-based filter proposed earlier. The performance of the proposed method is evaluated and compared with non-automatic methods of the state-of-the-art by means of objective measures and perceptual judgment on DIBCO databases and some document images collected from the web.

In this paper, we present efficient bit allocation methods for stereo image coding purpose. Since the common idea behind most of the existing stereo compression schemes consists of encoding a reference and residual images as well as a disparity map, we mainly focus on the bit allocation issue between the reference and residual images. Generally, this problem is solved in an empirical manner by looking for the optimal rates leading to the minimum distortion value. Thanks to recent approximations of the entropy and distortion functions, we propose accurate and fast bit allocation schemes appropriate for the open-loop- and closed-loop-based stereo coding structures. Experimental results show the benefits which can be drawn from the proposed bit allocation methods.

Many research works have been developed for stereo image compression purpose where most of them aim at encoding a reference image, a residual one and a disparity map. While the disparity field is often losslessly encoded, we are mainly interested in this paper in the bit allocation problem between the reference and residual images. Generally, the bit allocation is expressed as an optimization problem which involves the computation of the operational rate-distortion (RD) functions for all the wavelet subbands and for different quantization steps. However, this strategy is computationally intensive. To solve this problem, we consider the uniform scalar quantization of the wavelet subbands of both images modeled by a Generalized Gaussian distribution. Thanks to recent approximations of the entropy and distortion functions, we develop an optimal and fast bit allocation method. The obtained results confirm the efficiency of the proposed bit allocation method in the context of stereo image coding.

A novel objective measure for assessing the quality of image in-painting is proposed. In contrast to standard image quality metrics, the proposed one takes into account some constraints and characteristics related to the specific goals of inpainting techniques. The idea is to combine spatial low-level features and perceptual criteria in the design of the objective Image Inpainting Quality Metric (IIQM). The used characteristics are the visual coherence of the recovered regions and the visual saliency describing the visual importance of an area. Experimental results demonstrate the good performance of the proposed IIQM and its well adaptation to the evaluation of image inpainting results.

This paper presents a novel full-reference Stereo Image Quality Assessment (SIQA) measure based on well understood characteristics of the human visual system (HVS), namely contrast sensitivity and frequency and directional selectivity. Additionally, the proposed metric takes into account the stereo interplay between the two views, where one view may affect our perception of the overall quality of the stereo image pair. Therefore, a Binocular Just Noticeable Difference (BJND) model is used to compute the distortion visibility threshold, and the binocular suppression theory is considered in the proposed metric. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score provided by the database. The obtained experimental results show that the proposed metric correlates much better with the DMOS score than the state-of-the-art metrics do.

A new method for detecting and correcting color-timing mismatch in digital film is proposed. This method is based on a physical model which accounts for the absorption of light by the different layers of the film. The whole process consists of five sequential stages: shot change detection, registration, degraded zone detection, and unreliable motion detection and correction. One of the main issues in this paper is how to align the degraded and reference frames in the case of varying illumination conditions. A new transformation, based on the proposed physical model, is derived to make the data term of the optical flow framework robust to color change. The degraded regions are then detected and restored by using the computed flow field. The performance of the proposed method is evaluated through extensive tests on both simulated and real high definition films. The obtained results are very promising and confirm the efficiency of the proposed method.

Recently non-local means (NLM) has been known to be one of the most attractive denoising algorithms. It alters each pixel by a weighted average of pixels in the image. The weights express the level of similarity between two small patches defined for two involved pixels. There are many propositions to ameliorate the performance of this method. One of branches is to seek the whole image the most similar patches for a given one. In this paper, we investigate this approach and show that it is suitable for only highly textured images. Moreover, we show that combination of this approach and the original NLM yields better result for all image types.

In this paper, we introduce a radon wigner ville based image dissimilarity measure. The proposed Image distortion measure aims to combine the useful properties of the wigner ville and the directionality of the finite Radon transform for image quality assessment. The results are compared with several HVS and transform based measures of image quality on the basis of complexity of these measures and their consistency with subjective assessment.

Here we propose to apply the mean-shift algorithm to the four image subbands generated by a DWT, namely the LL, LH, HL and HH subbands. The simulated annealing technique traditionally used to identify the modes of the distribution for different resolutions can be performed by exploration of the multi-scale DWT pyramid, avoiding the costly estimation by a full mean-shift at each level.

In this paper, we conduct an investigation into background subtraction techniques using Gaussian Mixture Models (GMM) in the presence of large illumination changes and background variations. We show that the techniques used to date suffer from the trade-off imposed by the use of a common learning rate to update both the mean and variance of the component densities, which leads to a degeneracy of the variance and creates “saturated pixels”. To address this problem, we propose a simple yet effective technique that differentiates between the two learning rates, and imposes a constraint on the variance so as to avoid the degeneracy problem. Experimental results are presented which show that, compared to existing techniques, the proposed algorithm provides more robust segmentation in the presence of illumination variations and abrupt changes in background distribution.

We present in this paper a new global Full-Reference (FR) image quality metric (IQM) based on the fusion of several conventional FR metrics using an ANN learning algorithm. The fusion is shown to result in improved performance compared to individual FR metrics. Indeed, existing FR metrics can provide excellent results for specific degradations but poor results for others. Here, we propose to overcome this limitation by first improving the performance of existing FR metrics across different degradations through a ranking process. Then, using an Artificial Neural Network, we fuse the best-performing measures into a single metric called Global Index Quality Metric (G-IQM). The experimental results using the TID 2008 image database demonstrate that this new G-IQM metric achieves consistent image quality evaluation results with subjective evaluation.

A novel method for automatically detecting and restoring the color timing echo artifact which may result in the internegative/interpositive printing process is proposed. This degradation appears at shoot change, either on the top of the last frame of a shot or on the bottom of the subsequent frame of the next shot. In our work, the degraded region is firstly detected and then restored by using the histogram specification (HS) based method. To avoid discontinuities between the restored region and the remaining of the treated frame due to abrupt modification in a large region, the degraded zone is decomposed into several overlapped small blocks. Then a block matching algorithm is used and a block similarity metric based on ordering has also been proposed for motion estimation under luminance variation. The proposed method is tested on several color high definition (HD) films with highly promising results.

Image quality assessment is still an active field of research. The main objective of the developed image quality metric is to offer an index of quality that is consistent with the human subjective judgment of image quality. Despite the great number of developed metrics, there is still a need for image analysis tools that is able to extract the most perceptual relevant characteristics of an image. The goal of this work is then to propose a more advanced analysis and representation tools to extract more effective features that could be incorporated in the design of the image quality metric. In this paper, we propose a novel objective metric based on wave atoms transform. This new transform is half multiscale and half multi-directional. It offers a better representation of images containing oscillatory patterns and textures than the others known transforms. In this work, we propose a new full reference image quality metric based on wave atom transform and exploiting some properties of the human visual system. The consistency of the proposed metric with subjective evaluation is performed on LIVE database. The obtained correlation of this metric with the MOS provided by the database is better than other known metrics confirming thus the efficiency of this new image quality measure in predicting image quality.

A plethora of Image Quality Metrics (IQM) has been proposed during the last two decades. However, at present time, there is no accepted IQM able to predict the perceptual level of image degradation across different types of visual distortions. Some measures are more adapted for a set of degradations but inefficient for others. Indeed, the efficiency of any IQM has been shown to depend upon the type of degradation. Thus, we propose here a new approach for predicting the type of degradation before using IQMs. The basic idea is first to identify the type of distortion using a Bayesian approach, then select the most appropriate IQM for estimating image quality for that specific type of distortion. The performance of the proposed method is evaluated in terms of classification accuracy across different types of degradations.

This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.

A new method for Natural Rendering of Color Image based on Retinex (NRCIR) is proposed. Here, the word ¿natural¿ means that the ambience of image (warm or cold color impression) should not be changed after enhancement. Furthermore, the treatment should not introduce any additional light sources and should not produce halo effect or amplify blocking effect. Inspired by Retinex theory and histogram rescaling techniques, the proposed method tries to realize natural rendering of image with respect to the constraints listed above. Extensive tests with different types of natural images have been performed. The obtained results clearly demonstrate the efficiency of the proposed method.

A new method for deblocking is proposed. It aims to reduce the blocking artifacts in the compressed image by analyzing their visibility. A perceptual map is obtained using some Human Visual System (HVS) characteristics. This perceptual map is used as input to a recursive filter to reduce the blocking effect. The obtained results have been compared with a very recent efficient method considered.

The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to estimate the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.

This paper presents a new perceptual image watermarking scheme based on the Laplacian pyramid (LP) decomposition and a visibility map constructed using a human visual contrast model. This map is computed of each level of the pyramid in order to determine the auspicious regions for embedding the watermark. The spread spectrum technique is used to embed the watermark in some levels of the LP. The watermarked image is then constructed from the Laplacian images. The algorithm performances are evaluated in terms of watermark invisibility, using an objective image quality perceptual measure, the structural similarity index measure (SSIM), and robustness to different attacks of Stirmark such as Jpeg compression, low-pass filtering, additive noise and cropping.

A new pyramidal JND (just noticeable difference) model is presented. The idea is to use this JND to determine the optimum strength for embedding the watermark providing an invisible and robust watermarking scheme. The image is first decomposed into a multiresolution representation using the pyramidal decomposition. Then, a perceptual model is proposed to compute the JND value for each pixel at each Laplacian level. This model takes into account three main characteristics of the human visual system (HVS), namely: contrast sensitivity, luminance adaptation and contrast masking. The performance of the proposed technique is evaluated in terms of transparency, using subjective and objective tests, and robustness to different common attacks.

This paper focuses on the blind image separation using their sparse representation in an appropriate transform domain. A new separation method is proposed that proceeds in two steps: (i) an image pre-treatment step to transform the original sources into sparse images and to reduce the mixture matrix to an orthogonal transform (ii) and a separation step that exploits the transformed image sparsity via an lscrp-norm based contrast function. A simple and efficient natural gradient technique is used for the optimization of the contrast function. The resulting algorithm is shown to outperform existing techniques in terms of separation quality and computational cost.

In this paper we address a denoising technique based on calculation of non local means through neighborhoods. Non local neighborhoods are computed in a transformed domain, namely the wavelet domain. A noisy image is transformed using a lifting scheme. The wavelet coefficients in each subband image are modelized by a generalized Gaussian distribution (GGD) whose parameters (scale and shape parameters) are estimated using an appropriate technique. The estimated parameters are used to define a generalized non local mean which allows us to restore the original image. Processing in the wavelet domain is suitable since image are often available in a compressed domain, beside, processing smaller images allows us to reduce the computational cost

A new signal processing method is developed for solving the multiline fitting problem in a two dimensional image. We first reformulate the former problem in a special parameter estimation framework such that a first order or a second order polynomial phase signal structure is obtained. Then, the previously developed algorithms in that formalism (and particularly the downsampling technique for high resolution frequency estimation) can be exploited to produce accurate estimates for line parameters. This method is able to estimate the parameters of parallel lines with different offsets and handles the quantization noise effect which can not be done by the sensor array processing technique introduced by Aghajan et al. (1993). Simulation results are presented to demonstrate the usefulness of the proposed method.

A perceptual multiresolution filtering method (Moon and Spencer, 1945) based on human perception models is proposed. The main idea is to detect and remove the irrelevant structures at different scales using the just-noticeable contrast notion and luminance adaptation. The processing is done in the Laplacian and Gaussian pyramid decompositions of the image. The consistency of the method is demonstrated on a gray-level image.

An improvement of the well-known block truncation coding (BTC) method, introduced by Delp and Mitchell (1979), is proposed. The basic idea, derived from previous work of Beghdadi and Le Negrate (1989, 1991) on contrast enhancement and image filtering using a local contrast measure is to exploit edginess information in estimating both the quantization threshold and the reconstruction levels. The results obtained on actual images clearly show the efficiency of the method in preserving edge information compared to similar approaches.

This paper proposes a vector quantization (VQ) scheme that improves the quality of the reconstructed image by correcting quantization artifacts. The basic idea of the coding scheme is to treat separately the blocks containing edges (edge blocks), as they contain important perceptual information. The edge blocks are identified using an edge detection algorithm and are labeled by an escape index, and their reconstruction is done via an interpolation procedure that exploits the spatial correlation in the image. The remaining blocks (smooth blocks) are coded using standard VQ.

The difficulty of preserving edges is central to the problem of smoothing images. The main problem is that of distinguishing between meaningful contours and noise, so that the image can be smoothed without loss of details. Substantial efforts have been devoted to solving this difficult problem, and a plethora of filtering methods have been proposed in the literature. Non-linear filters have proved to be more efficient than their linear counterparts. Here, a new nonlinear filter for noise smoothing is introduced. This filter is based on the psychophysical phenomenon of human visual contrast sensitivity. Results on real images are presented to demonstrate the validity of our approach compared to other known filtering methods.

The purpose of the article is to give an analysis of the anisotropic diffusion (AD) and propose adaptive nonlinear filtering based on a judicious choice of the conductance function (CF) and the edgeness threshold. A new undesirable effect, which we call the "pinhole effect" may result when AD is introduced for the first time. A robust solution to this effect is proposed and evaluated through experimental data. The evolution of the diffused signal is analyzed through a physical model using the optical flow technique (OFT). The overall strategy is evaluated through experimental results obtained on synthetic and actual images.

The present paper provides a new smoothing method, based on psychophysical phenomena, in which the optimum smoothing parameters are automatically chosen. The method is a nonlinear one and it has some similarities with well-known morphological filters.

A nonlinear-noise filtering method for image processing, based on the entropy concept is developed and compared to the well-known median filter and to the center weighted median filter (CWM). The performance of the proposed method is evaluated through subjective and objective criteria. It is shown that this method performs better than the classical median for different types of noise and can perform better than the CWM filter in some cases.

We propose herein a new contour detection algorithm based on the human visual system. Using the proposed method, one can automatically select the thresholds that define the significant edges, such as perceived by the human eye. The threshold value is adapted to the background and surround intensities, according to criteria involved by Holladay's principle.