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Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain
Liu X.3; Wu X.1; Zhou J.2; Zhao D.3
2015-10-14
Conference NameIEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Source PublicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume07-12-June-2015
Pages5171-5178
Conference DateJUN 07-12, 2015
Conference PlaceBoston, MA
Abstract

Arguably the most common cause of image degradation is compression. This papers presents a novel approach to restoring JPEG-compressed images. The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images. The restoration is a sparse coding process carried out jointy in the DCT and. pixel domains. The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time using on-line machine-learnt local spatial features to regulate the solution of the underlying inverse problem. Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.

DOI10.1109/CVPR.2015.7299153
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000387959205024
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Citation statistics
Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.McMaster University
2.Universidade de Macau
3.Harbin Institute of Technology
Recommended Citation
GB/T 7714
Liu X.,Wu X.,Zhou J.,et al. Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain[C],2015:5171-5178.
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