NDLI: Image super-resolution using conditional generative adversarial network

Content Provider	IET Digital Library
Author	Qiao, Jiaojiao Song, Huihui Zhang, Kaihua Zhang, Xiaolu Liu, Qingshan
Abstract	Recently, extensive studies on a generative adversarial network (GAN) have made great progress in single image super-resolution (SISR). However, there still exists a significant difference between the reconstructed high-frequency and the real high-frequency details. To address this issue, this study presents an SISR approach based on conditional GAN (SRCGAN). SRCGAN includes a generator network that generates super-resolution (SR) images and a discriminator network that is trained to distinguish the SR images from ground-truth high-resolution (HR) ones. Specifically, the discriminator network uses the ground-truth HR image as a conditional variable, which guides the network to distinguish the real images from the SR images, facilitating training a more stable generator model than GAN without this guidance. Furthermore, a residual-learning module is introduced into the generator network to solve the issue of detail information loss in SR images. Finally, the network is trained in an end-to-end manner by optimizing a perceptual loss function. Extensive evaluations on four benchmark datasets including Set5, Set14, BSD100, and Urban100 demonstrate the superiority of the proposed SRCGAN over state-of-the-art methods in terms of PSNR, SSIM, and visual effect.
Starting Page	2673
Ending Page	2679
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6570
Journal	IET Image Processing
Publisher Date	2019-05-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Conditional GAN Conditional Generative Adversarial Network Discriminator Network Extensive Studies Generator Network Ground-truth High-resolution Ones Ground-truth HR Image High-frequency Details Image Reconstruction Image Resolution Knowledge Engineering Technique Learning in AI Optical, Image And Video Signal Processing Single Image Super-resolution SISR Approach SR Image SRCGAN Stable Generator Model Super-resolution Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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