Reduction of artifacts in cosine transform coded images
VIP '01 Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11
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
A smoothness constraint set based on local statistics of BDCT coefficients for image postprocessing
Image and Vision Computing
Block effect reduction by the 1-D gray polynomial interpolation
Digital Signal Processing
Image postprocessing by Non-local Kuan's filter
Journal of Visual Communication and Image Representation
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
Learning-based image restoration for compressed images
Image Communication
An efficient post-processing using DCT domain projections onto convex sets
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Image deblocking via sparse representation
Image Communication
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Since the postprocessing of coded images using a priori information depends on the constraints imposed on the coded images, it is important to utilize constraints that are best suited to postprocessing techniques. Among the constraint sets, the quantization constraint set (QCS) is commonly used in the iterative algorithms that are especially based on the theory of projections onto convex sets (POCS). The converged image in the iteration is usually a boundary point of the QCS. But, we can easily conjecture that the possible location of the original image is inside the QCS. In order to obtain an image inside the QCS, we proposed a new convex constraint set, a subset of the QCS called narrow QCS (NQCS) as a substitute for the QCS. In order to demonstrate that the NQCS works better than the QCS on natural images, we present mathematical analysis with examples and simulations by reformulating the iterative algorithm of the constrained minimization problem or of the POCS using the probability theory. Since the initial image of the iteration is the centroid of the QCS, we reach a conclusion that the first iteration is enough to recover the coded image, which implies no need of any theories that guarantee the convergences