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Proceedings Paper

Postprocessing and denoising of video using sparse multiresolutional transforms
Author(s): Osman G. Sezer; Onur G. Guleryuz
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Paper Abstract

This paper describes the construction of a set of sparsity-distortion-optimized orthonormal transforms designed for wavelet-domain image denoising. The optimization operates over sub-bands of given orientation and exploits intra-scale dependencies of wavelet coefficients over image singularities. When applied on the top of standard wavelet transforms, the resulting new sparse representation provides compaction that can be exploited in transform domain denoising via cycle-spinning.1 Our construction deviates from the literature, which mainly focuses on model-based methods, by offering a data-driven optimization of wavelet representations. Compared with translational-invariant denoising, the proposed method consistently offers better performance compared to the original wavelet-representation and can reach up to 3dB improvements.

Paper Details

Date Published: 7 September 2010
PDF: 8 pages
Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 77981M (7 September 2010); doi: 10.1117/12.863018
Show Author Affiliations
Osman G. Sezer, Georgia Tech (United States)
Onur G. Guleryuz, DoCoMo Communications Labs. USA, Inc. (United States)


Published in SPIE Proceedings Vol. 7798:
Applications of Digital Image Processing XXXIII
Andrew G. Tescher, Editor(s)

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