The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Aggelos K Katsaggelos - One of the best experts on this subject based on the ideXlab platform.
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variational bayesian Image Restoration with a product of spatially weighted total variation Image priors
IEEE Transactions on Image Processing, 2010Co-Authors: Giannis Chantas, Rafael Molina, N P Galatsanos, Aggelos K KatsaggelosAbstract:In this paper, a new Image prior is introduced and used in Image Restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local Image features than previous TV based priors. Bayesian inference is used for Image Restoration with this prior via the variational approximation. The proposed Restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. Numerical experiments are shown which demonstrate that Image Restoration based on this prior compares favorably with previous state-of-the-art Restoration algorithms.
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parameter estimation in tv Image Restoration using variational distribution approximation
IEEE Transactions on Image Processing, 2008Co-Authors: S D Babacan, Rafael Molina, Aggelos K KatsaggelosAbstract:In this paper, we propose novel algorithms for total variation (TV) based Image Restoration and parameter estimation utilizing variational distribution approximations. Within the hierarchical Bayesian formulation, the reconstructed Image and the unknown hyperparameters for the Image prior and the noise are simultaneously estimated. The proposed algorithms provide approximations to the posterior distributions of the latent variables using variational methods. We show that some of the current approaches to TV-based Image Restoration are special cases of our framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included.
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total variation Image Restoration and parameter estimation using variational posterior distribution approximation
International Conference on Image Processing, 2007Co-Authors: S D Babacan, Rafael Molina, Aggelos K KatsaggelosAbstract:In this paper we propose novel algorithms for total variation (TV) based Image Restoration and parameter estimation utilizing variational distribution approximations. By following the hierarchical Bayesian framework, we simultaneously estimate the reconstructed Image and the unknown hyper parameters for both the Image prior and the Image degradation noise. Our algorithms provide an approximation to the posterior distributions of the unknowns so that both the uncertainty of the estimates can be measured and different values from these distributions can be used for the estimates. We also show that some of the current approaches to TV-based Image Restoration are special cases of our variational framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyper parameters and clearly outperform existing methods when additional information is included.
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digital Image Restoration
IEEE Signal Processing Magazine, 1997Co-Authors: Mark R Banham, Aggelos K KatsaggelosAbstract:The article introduces digital Image Restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in Image Restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital Image Restoration algorithms that grow out of an area known as "regularized least squares" methods. It should be noted, however, that digital Image Restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature.
Guangming Shi - One of the best experts on this subject based on the ideXlab platform.
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Image Restoration via simultaneous sparse coding where structured sparsity meets gaussian scale mixture
International Journal of Computer Vision, 2015Co-Authors: Weisheng Dong, Guangming ShiAbstract:In Image processing, sparse coding has been known to be relevant to both variational and Bayesian approaches. The regularization parameter in variational Image Restoration is intrinsically connected with the shape parameter of sparse coefficients' distribution in Bayesian methods. How to set those parameters in a principled yet spatially adaptive fashion turns out to be a challenging problem especially for the class of nonlocal Image models. In this work, we propose a structured sparse coding framework to address this issue--more specifically, a nonlocal extension of Gaussian scale mixture (GSM) model is developed using simultaneous sparse coding (SSC) and its applications into Image Restoration are explored. It is shown that the variances of sparse coefficients (the field of scalar multipliers of Gaussians)--if treated as a latent variable--can be jointly estimated along with the unknown sparse coefficients via the method of alternating optimization. When applied to Image Restoration, our experimental results have shown that the proposed SSC---GSM technique can both preserve the sharpness of edges and suppress undesirable artifacts. Thanks to its capability of achieving a better spatial adaptation, SSC---GSM based Image Restoration often delivers reconstructed Images with higher subjective/objective qualities than other competing approaches.
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nonlocally centralized sparse representation for Image Restoration
IEEE Transactions on Image Processing, 2013Co-Authors: Weisheng Dong, Lei Zhang, Guangming ShiAbstract:Sparse representation models code an Image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various Image Restoration applications. However, due to the degradation of the observed Image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original Image. To improve the performance of sparse representation-based Image Restoration, in this paper the concept of sparse coding noise is introduced, and the goal of Image Restoration turns to how to suppress the sparse coding noise. To this end, we exploit the Image nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the original Image, and then centralize the sparse coding coefficients of the observed Image to those estimates. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on various types of Image Restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
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centralized sparse representation for Image Restoration
International Conference on Computer Vision, 2011Co-Authors: Weisheng Dong, Lei Zhang, Guangming ShiAbstract:This paper proposes a novel sparse representation model called centralized sparse representation (CSR) for Image Restoration tasks. In order for faithful Image reconstruction, it is expected that the sparse coding coefficients of the degraded Image should be as close as possible to those of the unknown original Image with the given dictionary. However, since the available data are the degraded (noisy, blurred and/or down-sampled) versions of the original Image, the sparse coding coefficients are often not accurate enough if only the local sparsity of the Image is considered, as in many existing sparse representation models. To make the sparse coding more accurate, a centralized sparsity constraint is introduced by exploiting the nonlocal Image statistics. The local sparsity and the nonlocal sparsity constraints are unified into a variational framework for optimization. Extensive experiments on Image Restoration validated that our CSR model achieves convincing improvement over previous state-of-the-art methods.
Weisheng Dong - One of the best experts on this subject based on the ideXlab platform.
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Image Restoration via simultaneous sparse coding where structured sparsity meets gaussian scale mixture
International Journal of Computer Vision, 2015Co-Authors: Weisheng Dong, Guangming ShiAbstract:In Image processing, sparse coding has been known to be relevant to both variational and Bayesian approaches. The regularization parameter in variational Image Restoration is intrinsically connected with the shape parameter of sparse coefficients' distribution in Bayesian methods. How to set those parameters in a principled yet spatially adaptive fashion turns out to be a challenging problem especially for the class of nonlocal Image models. In this work, we propose a structured sparse coding framework to address this issue--more specifically, a nonlocal extension of Gaussian scale mixture (GSM) model is developed using simultaneous sparse coding (SSC) and its applications into Image Restoration are explored. It is shown that the variances of sparse coefficients (the field of scalar multipliers of Gaussians)--if treated as a latent variable--can be jointly estimated along with the unknown sparse coefficients via the method of alternating optimization. When applied to Image Restoration, our experimental results have shown that the proposed SSC---GSM technique can both preserve the sharpness of edges and suppress undesirable artifacts. Thanks to its capability of achieving a better spatial adaptation, SSC---GSM based Image Restoration often delivers reconstructed Images with higher subjective/objective qualities than other competing approaches.
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nonlocally centralized sparse representation for Image Restoration
IEEE Transactions on Image Processing, 2013Co-Authors: Weisheng Dong, Lei Zhang, Guangming ShiAbstract:Sparse representation models code an Image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various Image Restoration applications. However, due to the degradation of the observed Image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original Image. To improve the performance of sparse representation-based Image Restoration, in this paper the concept of sparse coding noise is introduced, and the goal of Image Restoration turns to how to suppress the sparse coding noise. To this end, we exploit the Image nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the original Image, and then centralize the sparse coding coefficients of the observed Image to those estimates. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on various types of Image Restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
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centralized sparse representation for Image Restoration
International Conference on Computer Vision, 2011Co-Authors: Weisheng Dong, Lei Zhang, Guangming ShiAbstract:This paper proposes a novel sparse representation model called centralized sparse representation (CSR) for Image Restoration tasks. In order for faithful Image reconstruction, it is expected that the sparse coding coefficients of the degraded Image should be as close as possible to those of the unknown original Image with the given dictionary. However, since the available data are the degraded (noisy, blurred and/or down-sampled) versions of the original Image, the sparse coding coefficients are often not accurate enough if only the local sparsity of the Image is considered, as in many existing sparse representation models. To make the sparse coding more accurate, a centralized sparsity constraint is introduced by exploiting the nonlocal Image statistics. The local sparsity and the nonlocal sparsity constraints are unified into a variational framework for optimization. Extensive experiments on Image Restoration validated that our CSR model achieves convincing improvement over previous state-of-the-art methods.
Shuaiqi Liu - One of the best experts on this subject based on the ideXlab platform.
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total variation Image Restoration using hyper laplacian prior with overlapping group sparsity
Signal Processing, 2016Co-Authors: Mingzhu Shi, Tingting Han, Shuaiqi LiuAbstract:Image Restoration is a highly ill-posed problem and requires to be regularized. Many common Image priors aim to make full use of natural Image prior information. Total variation (TV) regularize prior has good performance of preserving edges but also has drawbacks in arising In this paper, we propose a total variation based Image Restoration method using hyper-Laplacian prior for Image gradient and the overlapping group sparsity prior for sparser Image representation constraint. We adopt the alternating direction method of multipliers (ADMM) method to optimize the object function of the proposed model and discuss the parameter selection criterion in the complex formulation. Finally, we carry out experiments on various degrade Images and compare our method with several classical state-of-the-art methods. Experimental results show that our method has good performance in convergence and suppressing staircase artifacts, which makes a good balance between alleviating staircase effects and preserving Image details. A total variation based Image Restoration method is proposed that using hyper-Laplacian prior for Image gradient and the overlapping group sparsity prior for sparser Image representation constraint.The alternating direction method of multipliers (ADMM) method is adopted to optimize the complex object function of the proposed model. Parameter selection criterions are discussed for the complex formulation.The method has good performance in convergence and suppressing staircase artifacts, which makes a good balance between alleviating staircase effects and preserving Image details.An Image Restoration method using hyper-Laplacian prior and overlapping group sparsity.
R S Schnerr - One of the best experts on this subject based on the ideXlab platform.
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stokes imaging polarimetry using Image Restoration a calibration strategy for fabry perot based instruments
Astronomy and Astrophysics, 2011Co-Authors: R S Schnerr, J De La Cruz Rodriguez, M Van NoortAbstract:Context. The combination of Image Restoration and a Fabry-Perot interferometer (FPI) based instrument in solar observations results in specific calibration issues. FPIs generally show variations over the field-of-view, while in the Image Restoration process, the 1-to-1 relation between pixel space and Image space is lost, thus complicating any correcting for such variations. Aims: We develop a data reduction method that takes these issues into account and minimizes the resulting errors. Methods: By accounting for the time variations in the telescope's Mueller matrix and using separate calibration data optimized for the wavefront sensing in the MOMFBD Image Restoration process and for the final deconvolution of the data, we have removed most of the calibration artifacts from the resulting data. Results: Using this method to reduce full Stokes data from CRISP at the SST, we find that it drastically reduces the instrumental and Image Restoration artifacts resulting from cavity errors, reflectivity variations, and the polarization dependence of flatfields. The results allow for useful scientific interpretation. Inversions of restored data from the δ sunspot AR11029 using the Nicole inversion code, reveal strong (~10 km s-1) downflows near the disk center side of the umbra. Conclusions: The use of Image Restoration in combination with an FPI-based instrument leads to complications in the calibrations and intrinsic limitations to the accuracy that can be achieved. We find that for CRISP, the resulting errors can be kept mostly below the polarimetric accuracy of ~10-3. Similar instruments aiming for higher polarimetric and high spectroscopic accuracy, will, however, need to take these problems into account.
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stokes imaging polarimetry using Image Restoration a calibration strategy for fabry p e rot based instruments
arXiv: Solar and Stellar Astrophysics, 2010Co-Authors: R S Schnerr, J De La Cruz Rodriguez, M Van NoortAbstract:context: The combination of Image Restoration and a Fabry-P\'{e}rot interferometer (FPI) based instrument in solar observations results in specific calibration issues. FPIs generally show variations over the field-of-view, while in the Image Restoration process, the 1-to-1 relation between pixel space and Image space is lost, thus complicating any correcting for such variations. aims: We develop a data reduction method that takes these issues into account and minimizes the resulting errors. methods: By accounting for the time variations in the telescope's Mueller matrix and using separate calibration data optimized for the wavefront sensing in the MOMFBD Image Restoration process and for the final deconvolution of the data, we have removed most of the calibration artifacts from the resulting data. results: Using this method to reduce full Stokes data from CRISP at the SST, we find that it drastically reduces the instrumental and Image Restoration artifacts resulting from cavity errors, reflectivity variations, and the polarization dependence of flatfields. The results allow for useful scientific interpretation. Inversions of restored data from the $\delta$ sunspot AR11029 using the Nicole inversion code, reveal strong (~10 km/s) downflows near the disk center side of the umbra. conclusions: The use of Image Restoration in combination with an FPI-based instrument leads to complications in the calibrations and intrinsic limitations to the accuracy that can be achieved. We find that for CRISP, the resulting errors can be kept mostly below the polarimetric accuracy of ~10^-3. Similar instruments aiming for higher polarimetric and high spectroscopic accuracy, will, however, need to take these problems into account. keywords: Techniques: Image processing, polarimetric, imaging spectroscopy, Sun: surface magnetism, sunspots, activity
