Sparse and Redundant Representations and Motion-Estimation-Free Algorithm for Video Denoising. Protter, M. & Elad, M. In Wavelets XII, volume 6701, pages 496–507, September, 2007. SPIE. ![Sparse and Redundant Representations and Motion-Estimation-Free Algorithm for Video Denoising [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{protter_sparse_2007,
title = {Sparse and {Redundant} {Representations} and {Motion}-{Estimation}-{Free} {Algorithm} for {Video} {Denoising}},
volume = {6701},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6701/67011D/Sparse-and-Redundant-Representations-and-Motion-Estimation-Free-Algorithm-for/10.1117/12.731851.full},
doi = {10.1117/12.731851},
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.},
language = {en},
urldate = {2023-08-07},
booktitle = {Wavelets {XII}},
publisher = {SPIE},
author = {Protter, Matan and Elad, Michael},
month = sep,
year = {2007},
keywords = {\#Representation{\textgreater}Denoising, \#Sparse, \#Video, /unread},
pages = {496--507},
}
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