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4 March 2015 Restoration of block-transform compressed images via homotopic regularized sparse reconstruction
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Proceedings Volume 9410, Visual Information Processing and Communication VI; 941005 (2015) https://doi.org/10.1117/12.2082861
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
Block-transform lossy image compression is the most widely-used approach for compressing and storing images or video. A novel algorithm to restore highly compressed images with greater image quality is proposed. Since many block-transform coefficients are reduced to zero after quantization, the compressed image restoration problem can be treated as a sparse reconstruction problem where the original image is reconstructed based on sparse, degraded measurements in the form of highly quantized block-transform coefficients. The sparse reconstruction problem is solved by minimizing a homotopic regularized function, subject to data fidelity in the block-transform domain. Experimental results using compressed natural images at di erent levels of compression show improved performance by using the proposed algorithm compared to other methods.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey Glaister, Shahid A. Haider, Alexander Wong, and David A. Clausi "Restoration of block-transform compressed images via homotopic regularized sparse reconstruction", Proc. SPIE 9410, Visual Information Processing and Communication VI, 941005 (4 March 2015); https://doi.org/10.1117/12.2082861
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