Reduction of blocking artifacts in JPEG compressed images
Digital Signal Processing
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
Local MAP estimation for quality improvement of compressed color images
Pattern Recognition
Learning-based image restoration for compressed images
Image Communication
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The reconstruction of images from incomplete block discrete cosine transform (BDCT) data is examined. The problem is formulated as one of regularized image recovery. According to this formulation, the image in the decoder is reconstructed by using not only the transmitted data but also prior knowledge about the smoothness of the original image, which complements the transmitted data. Two methods are proposed for solving this regularized recovery problem. The first is based on the theory of projections onto convex sets (POCS) while the second is based on the constrained least squares (CLS) approach. For the POCS-based method, a new constraint set is defined that conveys smoothness information not captured by the transmitted BDCT coefficients, and the projection onto it is computed. For the CLS method an objective function is proposed that captures the smoothness properties of the original image. Iterative algorithms are introduced for its minimization. Experimental results are presented