Regularized Compression of A Noisy Blurred Image

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Titel: Regularized Compression of A Noisy Blurred Image
Autoren: Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani
Verlagsinformationen: Zenodo
Publikationsjahr: 2016
Bestand: Zenodo
Schlagwörter: Image Regularization, Image Compression, Nonnegative Matrix Factorization
Beschreibung: Both regularization and compression are important issues in image processing and have been widely approached in the literature. The usual procedure to obtain the compression of an image given through a noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the regularized image to get an approximation in terms of low rank nonnegative factors. We examine here the possibility of swapping the two steps by deblurring directly the noisy factors or partially denoised factors. The experimentation shows that in this way images with comparable regularized compression can be obtained with a lower computational cost.
Publikationsart: article in journal/newspaper
Sprache: unknown
Relation: https://zenodo.org/records/2869756; oai:zenodo.org:2869756; https://doi.org/10.5281/zenodo.2869756
DOI: 10.5281/zenodo.2869756
Verfügbarkeit: https://doi.org/10.5281/zenodo.2869756
https://zenodo.org/records/2869756
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Dokumentencode: edsbas.686B6E8E
Datenbank: BASE
Beschreibung
Abstract:Both regularization and compression are important issues in image processing and have been widely approached in the literature. The usual procedure to obtain the compression of an image given through a noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the regularized image to get an approximation in terms of low rank nonnegative factors. We examine here the possibility of swapping the two steps by deblurring directly the noisy factors or partially denoised factors. The experimentation shows that in this way images with comparable regularized compression can be obtained with a lower computational cost.
DOI:10.5281/zenodo.2869756