Deep unfolding multi-scale regularizer network for image denoising
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2023
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oai:localhost:PNK-73112023-03-30T02:02:21Z Deep unfolding multi-scale regularizer network for image denoising Jingzhao, Xu Mengke, Yuan Dong-Ming, Yan CNNs DUMRN CC BY Existing deep unfolding methods unroll an optimization algorithm with a fixed number of steps, and utilize convolutional neural networks (CNNs) to learn data-driven priors. However, their performance is limited for two main reasons. Firstly, priors learned in deep feature space need to be converted to the image space at each iteration step, which limits the depth of CNNs and prevents CNNs from exploiting contextual information. Secondly, existing methods only learn deep priors at the single full-resolution scale, so ignore the benefits of multi-scale context in dealing with high level noise. To address these issues, we explicitly consider the image denoising process in the deep feature space and propose the deep unfolding multi-scale regularizer network (DUMRN) for image denoising 2023-03-30T02:02:21Z 2023-03-30T02:02:21Z 2023 Book https://link.springer.com/article/10.1007/s41095-022-0277-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7311 en application/pdf Springer |
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Digital Phenikaa |
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CNNs DUMRN |
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CNNs DUMRN Jingzhao, Xu Mengke, Yuan Dong-Ming, Yan Deep unfolding multi-scale regularizer network for image denoising |
description |
CC BY |
format |
Book |
author |
Jingzhao, Xu Mengke, Yuan Dong-Ming, Yan |
author_facet |
Jingzhao, Xu Mengke, Yuan Dong-Ming, Yan |
author_sort |
Jingzhao, Xu |
title |
Deep unfolding multi-scale regularizer network for image denoising |
title_short |
Deep unfolding multi-scale regularizer network for image denoising |
title_full |
Deep unfolding multi-scale regularizer network for image denoising |
title_fullStr |
Deep unfolding multi-scale regularizer network for image denoising |
title_full_unstemmed |
Deep unfolding multi-scale regularizer network for image denoising |
title_sort |
deep unfolding multi-scale regularizer network for image denoising |
publisher |
Springer |
publishDate |
2023 |
url |
https://link.springer.com/article/10.1007/s41095-022-0277-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7311 |
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1761821912054366208 |
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8.891145 |