Models for patch-based image restoration
Journal on Image and Video Processing - Special issue on patches in vision
Short Communication: Efficient quadtree based block-shift filtering for deblocking and deringing
Journal of Visual Communication and Image Representation
Reconstructing videos from multiple compressed copies
IEEE Transactions on Circuits and Systems for Video Technology
Composite modeling of optical flow for artifacts reduction
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Image deblocking by the dual adaptive FIR wiener filter and overcomplete representation
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Image postprocessing by Non-local Kuan's filter
Journal of Visual Communication and Image Representation
Video enhancement from multiple compressed copies in transform domain
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Image deblocking via sparse representation
Image Communication
Advanced algorithms and their parallel architectures for full-HD TV video display processing
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Image enhancement and post-processing for low-resolution compressed video
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Perceptually optimized blind repair of natural images
Image Communication
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The block-based discrete cosine transform (BDCT) is often used in image and video coding. It may introduce block artifacts at low data rates that manifest themselves as an annoying discontinuity between adjacent blocks. In this paper, we address this problem by investigating a transform-domain Markov random field (TD-MRF) model. Based on this model, two block artifact reduction postprocessing methods are presented. The first method, referred to as TD-MRF, provides an efficient progressive transform-domain solution. Our experimental results show that TD-MRF can reduce up to 90% of the computational complexity compared with spatial-domain MRF (SD-MRF) methods while still achieving comparable visual quality improvements. We then discuss a hybrid framework, referred to as TSD-MRF, that exploits the advantages of both TD-MRF and SD-MRF. The experimental results confirm that TSD-MRF can improve visual quality both objectively and subjectively over SD-MRF methods.