The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Rafael Molina - One of the best experts on this subject based on the ideXlab platform.
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A Bayesian approach for the estimation and transmission of regularization parameters for reducing blocking artifacts
IEEE Transactions on Image Processing, 2000Co-Authors: Javier Mateos, Aggelos K. Katsaggelos, Rafael MolinaAbstract:With block-based compression approaches for both still images and sequences of images annoying blocking artifacts are exhibited, primarily at high compression ratios. They are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. We propose the application of the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform (BDCT) compressed images and the estimation of the required parameters. We derive expressions for the Iterative Evaluation of these parameters applying the evidence analysis within the hierarchical Bayesian paradigm. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
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Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing, 1999Co-Authors: Rafael Molina, Aggelos K. Katsaggelos, Javier MateosAbstract:In this paper, we propose the application of the hierarchical Bayesian paradigm to the image restoration problem. We derive expressions for the Iterative Evaluation of the two hyperparameters applying the evidence and maximum a posteriori (MAP) analysis within the hierarchical Bayesian paradigm. We show analytically that the analysis provided by the evidence approach is more realistic and appropriate than the MAP approach for the image restoration problem. We furthermore study the relationship between the evidence and an Iterative approach resulting from the set theoretic regularization approach for estimating the two hyperparameters, or their ratio, defined as the regularization parameter. Finally the proposed algorithms are tested experimentally.
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Removal of Blocking Artifacts Using a Hierarchical Bayesian Approach
Signal Recovery Techniques for Image and Video Compression and Transmission, 1998Co-Authors: Rafael Molina, Aggelos K. Katsaggelos, Javier MateosAbstract:Blocking artifacts are exhibited by block-compressed still images and sequences of images, primarily at high compression ratios. These artifacts are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. In this chapter, we provide a survey of the literature on the removal of such artifacts. We also apply the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform compressed images and the estimation of the required hyperparameters. The evidence analysis within the hierarchical Bayesian paradigm is used to derive expressions for the Iterative Evaluation of these parameters. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
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Hierarchical Bayesian approach to image restoration and the Iterative Evaluation of the regularization parameter
Visual Communications and Image Processing '94, 1994Co-Authors: Rafael Molina, Aggelos K. KatsaggelosAbstract:In an image restoration problem we usually have two different kinds of information. In the first stage, we have knowledge about the structural form of the noise and local characteristics of the image. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on these hyperparameters, through which information about these hyperparameters is included. In this work we relate the hierarchical Bayesian approach to image restoration to an Iterative approach for estimating these hyperparameters in a deterministic way.
Javier Mateos - One of the best experts on this subject based on the ideXlab platform.
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A Bayesian approach for the estimation and transmission of regularization parameters for reducing blocking artifacts
IEEE Transactions on Image Processing, 2000Co-Authors: Javier Mateos, Aggelos K. Katsaggelos, Rafael MolinaAbstract:With block-based compression approaches for both still images and sequences of images annoying blocking artifacts are exhibited, primarily at high compression ratios. They are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. We propose the application of the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform (BDCT) compressed images and the estimation of the required parameters. We derive expressions for the Iterative Evaluation of these parameters applying the evidence analysis within the hierarchical Bayesian paradigm. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
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Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing, 1999Co-Authors: Rafael Molina, Aggelos K. Katsaggelos, Javier MateosAbstract:In this paper, we propose the application of the hierarchical Bayesian paradigm to the image restoration problem. We derive expressions for the Iterative Evaluation of the two hyperparameters applying the evidence and maximum a posteriori (MAP) analysis within the hierarchical Bayesian paradigm. We show analytically that the analysis provided by the evidence approach is more realistic and appropriate than the MAP approach for the image restoration problem. We furthermore study the relationship between the evidence and an Iterative approach resulting from the set theoretic regularization approach for estimating the two hyperparameters, or their ratio, defined as the regularization parameter. Finally the proposed algorithms are tested experimentally.
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Removal of Blocking Artifacts Using a Hierarchical Bayesian Approach
Signal Recovery Techniques for Image and Video Compression and Transmission, 1998Co-Authors: Rafael Molina, Aggelos K. Katsaggelos, Javier MateosAbstract:Blocking artifacts are exhibited by block-compressed still images and sequences of images, primarily at high compression ratios. These artifacts are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. In this chapter, we provide a survey of the literature on the removal of such artifacts. We also apply the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform compressed images and the estimation of the required hyperparameters. The evidence analysis within the hierarchical Bayesian paradigm is used to derive expressions for the Iterative Evaluation of these parameters. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
Aggelos K. Katsaggelos - One of the best experts on this subject based on the ideXlab platform.
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A Bayesian approach for the estimation and transmission of regularization parameters for reducing blocking artifacts
IEEE Transactions on Image Processing, 2000Co-Authors: Javier Mateos, Aggelos K. Katsaggelos, Rafael MolinaAbstract:With block-based compression approaches for both still images and sequences of images annoying blocking artifacts are exhibited, primarily at high compression ratios. They are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. We propose the application of the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform (BDCT) compressed images and the estimation of the required parameters. We derive expressions for the Iterative Evaluation of these parameters applying the evidence analysis within the hierarchical Bayesian paradigm. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
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Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing, 1999Co-Authors: Rafael Molina, Aggelos K. Katsaggelos, Javier MateosAbstract:In this paper, we propose the application of the hierarchical Bayesian paradigm to the image restoration problem. We derive expressions for the Iterative Evaluation of the two hyperparameters applying the evidence and maximum a posteriori (MAP) analysis within the hierarchical Bayesian paradigm. We show analytically that the analysis provided by the evidence approach is more realistic and appropriate than the MAP approach for the image restoration problem. We furthermore study the relationship between the evidence and an Iterative approach resulting from the set theoretic regularization approach for estimating the two hyperparameters, or their ratio, defined as the regularization parameter. Finally the proposed algorithms are tested experimentally.
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Removal of Blocking Artifacts Using a Hierarchical Bayesian Approach
Signal Recovery Techniques for Image and Video Compression and Transmission, 1998Co-Authors: Rafael Molina, Aggelos K. Katsaggelos, Javier MateosAbstract:Blocking artifacts are exhibited by block-compressed still images and sequences of images, primarily at high compression ratios. These artifacts are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. In this chapter, we provide a survey of the literature on the removal of such artifacts. We also apply the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform compressed images and the estimation of the required hyperparameters. The evidence analysis within the hierarchical Bayesian paradigm is used to derive expressions for the Iterative Evaluation of these parameters. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
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Hierarchical Bayesian approach to image restoration and the Iterative Evaluation of the regularization parameter
Visual Communications and Image Processing '94, 1994Co-Authors: Rafael Molina, Aggelos K. KatsaggelosAbstract:In an image restoration problem we usually have two different kinds of information. In the first stage, we have knowledge about the structural form of the noise and local characteristics of the image. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on these hyperparameters, through which information about these hyperparameters is included. In this work we relate the hierarchical Bayesian approach to image restoration to an Iterative approach for estimating these hyperparameters in a deterministic way.
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Iterative Evaluation of the regularization parameter in regularized image restoration
Journal of Visual Communication and Image Representation, 1992Co-Authors: Aggelos K. Katsaggelos, Moon Gi KangAbstract:In this paper a nonlinear regularized Iterative image restoration algorithm is proposed, according to which no prior knowledge about the noise variance is assumed. The algorithm results from a set-theoretic regularization approach, where bounds of the stabilizing functional and the noise variance, which determine the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm, as well as an optimality criterion for the regularization parameter, are derived and experimental results are shown.
Wolfgang Nejdl - One of the best experts on this subject based on the ideXlab platform.
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ArchiveWeb: collaboratively extending and exploring web archive collections—How would you like to work with your collections?
International Journal on Digital Libraries, 2018Co-Authors: Zeon Trevor Fernando, Ivana Marenzi, Wolfgang NejdlAbstract:Curated web archive collections contain focused digital content which is collected by archiving organizations, groups, and individuals to provide a representative sample covering specific topics and events to preserve them for future exploration and analysis. In this paper, we discuss how to best support collaborative construction and exploration of these collections through the ArchiveWeb system. ArchiveWeb has been developed using an Iterative Evaluation-driven design-based research approach, with considerable user feedback at all stages. The first part of this paper describes the important insights we gained from our initial requirements engineering phase during the first year of the project and the main functionalities of the current ArchiveWeb system for searching, constructing, exploring, and discussing web archive collections. The second part summarizes the feedback we received on this version from archiving organizations and libraries, as well as our corresponding plans for improving and extending the system for the next release.
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TPDL - ArchiveWeb: Collaboratively Extending and Exploring Web Archive Collections
Research and Advanced Technology for Digital Libraries, 2016Co-Authors: Zeon Trevor Fernando, Ivana Marenzi, Wolfgang Nejdl, Rishita KalyaniAbstract:Curated web archive collections contain focused digital contents which are collected by archiving organizations to provide a representative sample covering specific topics and events to preserve them for future exploration and analysis. In this paper, we discuss how to best support collaborative construction and exploration of these collections through the ArchiveWeb system. ArchiveWeb has been developed using an Iterative Evaluation-driven design-based research approach, with considerable user feedback at all stages. This paper describes the functionalities of our current prototype for searching, constructing, exploring and discussing web archive collections, as well as feedback on this prototype from seven archiving organizations, and our plans for improving the next release of the system.
Zeon Trevor Fernando - One of the best experts on this subject based on the ideXlab platform.
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ArchiveWeb: collaboratively extending and exploring web archive collections—How would you like to work with your collections?
International Journal on Digital Libraries, 2018Co-Authors: Zeon Trevor Fernando, Ivana Marenzi, Wolfgang NejdlAbstract:Curated web archive collections contain focused digital content which is collected by archiving organizations, groups, and individuals to provide a representative sample covering specific topics and events to preserve them for future exploration and analysis. In this paper, we discuss how to best support collaborative construction and exploration of these collections through the ArchiveWeb system. ArchiveWeb has been developed using an Iterative Evaluation-driven design-based research approach, with considerable user feedback at all stages. The first part of this paper describes the important insights we gained from our initial requirements engineering phase during the first year of the project and the main functionalities of the current ArchiveWeb system for searching, constructing, exploring, and discussing web archive collections. The second part summarizes the feedback we received on this version from archiving organizations and libraries, as well as our corresponding plans for improving and extending the system for the next release.
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TPDL - ArchiveWeb: Collaboratively Extending and Exploring Web Archive Collections
Research and Advanced Technology for Digital Libraries, 2016Co-Authors: Zeon Trevor Fernando, Ivana Marenzi, Wolfgang Nejdl, Rishita KalyaniAbstract:Curated web archive collections contain focused digital contents which are collected by archiving organizations to provide a representative sample covering specific topics and events to preserve them for future exploration and analysis. In this paper, we discuss how to best support collaborative construction and exploration of these collections through the ArchiveWeb system. ArchiveWeb has been developed using an Iterative Evaluation-driven design-based research approach, with considerable user feedback at all stages. This paper describes the functionalities of our current prototype for searching, constructing, exploring and discussing web archive collections, as well as feedback on this prototype from seven archiving organizations, and our plans for improving the next release of the system.
