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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3051767
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Guo, Yiwen
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Deep Likelihood Network for Image Restoration With Multiple Degradation Levels
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|c 2021
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 08.02.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Convolutional neural networks have been proven effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single particular degradation level, and their performance deteriorates drastically when applied to other degradation settings. In this paper, we propose deep likelihood network (DL-Net), aiming at generalizing off-the-shelf image restoration networks to succeed over a spectrum of degradation levels. We slightly modify an off-the-shelf network by appending a simple recursive module, which is derived from a fidelity term, for disentangling the computation for multiple degradation levels. Extensive experimental results on image inpainting, interpolation, and super-resolution show the effectiveness of our DL-Net
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|a Journal Article
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|a Lu, Ming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zuo, Wangmeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Changshui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Yurong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 21., Seite 2669-2681
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:30
|g year:2021
|g day:21
|g pages:2669-2681
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|u http://dx.doi.org/10.1109/TIP.2021.3051767
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|d 30
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|b 21
|h 2669-2681
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