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Proceedings Paper

Sparse and redundant representations and motion-estimation-free algorithm for video denoising
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Paper Abstract

The quality of video sequences (e.g. old movies, webcam, TV broadcast) is often reduced by noise, usually assumed white and Gaussian, being superimposed on the sequence. When denoising image sequences, rather than a single image, the temporal dimension can be used for gaining in better denoising performance, as well as in the algorithms' speed. This paper extends single image denoising method reported in to sequences. This algorithm relies on sparse and redundant representations of small patches in the images. Three different extensions are offered, and all are tested and found to lead to substantial benefits both in denoising quality and algorithm complexity, compared to running the single image algorithm sequentially. After these modifications, the proposed algorithm displays state-of-the-art denoising performance, while not relying on motion estimation.

Paper Details

Date Published: 20 September 2007
PDF: 12 pages
Proc. SPIE 6701, Wavelets XII, 67011D (20 September 2007); doi: 10.1117/12.731851
Show Author Affiliations
Matan Protter, The Technion-Israel Institute of Technology (Israel)
Michael Elad, The Technion-Israel Institute of Technology (Israel)


Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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