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Proceedings Paper

Single image super-resolution based on enhanced deep residual GAN
Author(s): Zhiyong Chen; Jing Hu; Xuyang Zhang; Xiangjun Li
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Paper Abstract

With the application of image more and more widely, People put forward higher requirements on the image quality of small objects and details in the image. In recent years, with the development of deep learning, it achieved good results in the research of image super-resolution. In this paper, we proposed EDSRGAN, a single image super-resolution(SISR) algorithm, based on enhanced residual network and the adversarial network. Compared with SRGAN, which is also based on the adversarial network, EDSRGAN can greatly reduce the high-frequency noise contained in the super-resolution(SR) image, and it also leads SRGAN in terms of peak signal to noise ratio and structural similarity evaluation indicators. Although EDSRGAN lagged behind EDSR in terms of peak signal to noise ratio and structural similarity, the SR images generated by EDSRGAN were sharper than EDSR in the object edges and targets details. EDSRGAN could achieve good results in image super-resolution on small targets.

Paper Details

Date Published: 14 February 2020
PDF: 9 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301W (14 February 2020); doi: 10.1117/12.2541900
Show Author Affiliations
Zhiyong Chen, Huazhong Univ. of Science and Technology (China)
Jing Hu, Huazhong Univ. of Science and Technology (China)
National Key Lab. of Science and Technology on Multi-spectral Information Processing (China)
Xuyang Zhang, Huazhong Univ. of Science and Technology (China)
Xiangjun Li, Yan'an Univ. (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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