NDLI: Generative adversarial image super-resolution network for multiple degradations

Content Provider	IET Digital Library
Author	Lin, Hong Fan, Jing Zhang, Yangyi Peng, Dewei
Abstract	The existing single image super-resolution methods based on deep learning cannot handle multiple degradations well, and the generated image tends to be blurred and over-smoothed due to poor generalisation ability. In this study, the authors propose a method based on a generative adversarial network (GAN) to deal with multiple degradations. In the generator network, blur kernel and noise level are used as input through dimensionality stretching strategy preprocessing to make full use of prior knowledge. In addition, three discriminators with different scales are used in the discriminator network to pay attention to the reconstruction of image details while focusing on the global consistency of the image. For the problems of vanishing gradient and mode collapse existing in GAN-based methods, a gradient penalty term is added in the loss function. Extensive experiments demonstrate that the proposed method not only can handle multiple degradations to obtain state-of-the-art performance, but also deliver visually credible results in real scenes.
Starting Page	4520
Ending Page	4527
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.1176
Journal	IET Image Processing
Publisher Date	2020-12-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Discriminator Network Existing Single Image Super-resolution Method GAN-based Method Generative Adversarial Image Super-resolution Network Generative Adversarial Network Generator Network Image Details Image Reconstruction Image Resolution Image Restoration Image Texture Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Multiple Degradations Numerical Analysis Optical, Image And Video Signal Processing Poor Generalisation Ability
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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