Deep learning on image denoising: An overview

Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaus...

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Vydáno v:	Neural networks Ročník 131; s. 251 - 275
Hlavní autoři:	Tian, Chunwei, Fei, Lunke, Zheng, Wenxian, Xu, Yong, Zuo, Wangmeng, Lin, Chia-Wen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.11.2020
Témata:	Blind denoising Deep Learning Hybrid noisy images Image denoising Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Real noisy images Signal-To-Noise Ratio Deep learning Real noisy images Blind denoising Hybrid noisy images Image denoising
ISSN:	0893-6080, 1879-2782, 1879-2782
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Shrnutí:	Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2020.07.025