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Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution
,
Bowen Wan,
Daming Shi,
Cheng Cheng
Remote Sensing, Volume: 15, Issue: 2, Start page: 364
Swansea University Author:
Cheng Cheng
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© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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DOI (Published version): 10.3390/rs15020364
Abstract
With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is c...
| Published in: | Remote Sensing |
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| ISSN: | 2072-4292 |
| Published: |
MDPI AG
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65952 |
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| Abstract: |
With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising. |
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| Keywords: |
image denoising network; unsupervised; pseudo supervised |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work is supported by the Ministry of Science and Technology China (MOST) Major Program on New Generation of Artificial Intelligence 2030 No. 2018AAA0102200. It is also supported by the Natural Science Foundation China (NSFC) Major Project No. 61827814 and the Shenzhen Science and Technology Innovation Commission (SZSTI) Project No. JCYJ20190808153619413. The experiments in this work were conducted at the National Engineering Laboratory for Big Data System Computing Technology, China. |
| Issue: |
2 |
| Start Page: |
364 |