NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION

NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION

We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching appr...

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Main Authors: A. V. Nasonov, O. S. Volodina, A. S. Krylov
Format: Article
Language:English
Published: Copernicus Publications 2021-04-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-2-W1-2021/167/2021/isprs-archives-XLIV-2-W1-2021-167-2021.pdf
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spelling doaj-45f2c9d12e984c62b5a0010b0f26a8302021-04-15T22:02:27ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-04-01XLIV-2-W1-202116717010.5194/isprs-archives-XLIV-2-W1-2021-167-2021NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATIONA. V. Nasonov0O. S. Volodina1A. S. Krylov2Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, RussiaLaboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, RussiaLaboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, RussiaWe address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-2-W1-2021/167/2021/isprs-archives-XLIV-2-W1-2021-167-2021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. V. Nasonov
O. S. Volodina
A. S. Krylov
spellingShingle A. V. Nasonov
O. S. Volodina
A. S. Krylov
NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet A. V. Nasonov
O. S. Volodina
A. S. Krylov
author_sort A. V. Nasonov
title NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION
title_short NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION
title_full NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION
title_fullStr NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION
title_full_unstemmed NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION
title_sort non-linear multi-frame image denoising using weighted nuclear norm minimization
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2021-04-01
description We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-2-W1-2021/167/2021/isprs-archives-XLIV-2-W1-2021-167-2021.pdf
work_keys_str_mv AT avnasonov nonlinearmultiframeimagedenoisingusingweightednuclearnormminimization
AT osvolodina nonlinearmultiframeimagedenoisingusingweightednuclearnormminimization
AT askrylov nonlinearmultiframeimagedenoisingusingweightednuclearnormminimization
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