Sparse and Redundant Representations and Motion-Estimation-Free Algorithm for Video Denoising

Matan Protter; Michael Elad

doi:10.1117/12.731851

20 September 2007 Sparse and redundant representations and motion-estimation-free algorithm for video denoising

Matan Protter, Michael Elad

Proceedings Volume 6701, Wavelets XII; 67011D (2007) https://doi.org/10.1117/12.731851
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

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.

Citation Download Citation

Matan Protter and Michael Elad "Sparse and redundant representations and motion-estimation-free algorithm for video denoising", Proc. SPIE 6701, Wavelets XII, 67011D (20 September 2007); https://doi.org/10.1117/12.731851

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