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Proceedings Paper

Restoration of block-transform compressed images via homotopic regularized sparse reconstruction
Author(s): Jeffrey Glaister; Shahid A. Haider; Alexander Wong; David A. Clausi
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Paper 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.

Paper Details

Date Published: 4 March 2015
PDF: 15 pages
Proc. SPIE 9410, Visual Information Processing and Communication VI, 941005 (4 March 2015); doi: 10.1117/12.2082861
Show Author Affiliations
Jeffrey Glaister, Univ. of Waterloo (Canada)
Shahid A. Haider, Univ. of Waterloo (Canada)
Alexander Wong, Univ. of Waterloo (Canada)
David A. Clausi, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 9410:
Visual Information Processing and Communication VI
Amir Said; Onur G. Guleryuz; Robert L. Stevenson, Editor(s)

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