On the application of structured sparse model selection to JPEG compressed images
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Reduction of JPEG compression artifacts by kernel regression and probabilistic self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Adaptive coded aperture photography
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Learning-based image restoration for compressed images
Image Communication
Adaptive non-local means filter for image deblocking
Image Communication
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We propose an effective deblocking scheme with extremely low computational complexity. The algorithm involves three parts: local ac coefficient regularization (ACR) of shifted blocks in the discrete cosine transform (DCT) domain, block-wise shape adaptive filtering (BSAF) in the spatial domain, and quantization constraint (QC) in the DCT domain. The DCT domain ACR suppresses the grid noise (blockiness) in monotone areas. The spatial-domain BSAF alleviates the staircase noise along the edge, and the ringing near the edge and the corner outliers. The narrow quantization constraint set is imposed to prevent possible oversmoothing and improve PSNR performance. Extensive simulation results and comparative studies are provided to justify the effectiveness and efficiency of the proposed deblocking algorithm.