Deep Likelihood Network for Image Restoration With Multiple Degradation Levels

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....

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on image processing Vol. 30; pp. 2669 - 2681
Main Authors Guo, Yiwen, Lu, Ming, Zuo, Wangmeng, Zhang, Changshui, Chen, Yurong
Format Journal Article
LanguageEnglish
Published United States IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Computer architecture
Convolution
Deep learning
Degradation
Image inpainting
image interpolation
Image restoration
image super-resolution
Interpolation
likelihood
multiple degradation levels
Task analysis
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3051767