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Single-image super-resolution reconstruction via generative adversarial network
Author(s): Chunwu Ju; Xiuqin Su; Haoyuan Yang; Hailong Ning
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Paper Abstract

Single-image super-resolution (SISR) reconstruction is important for image processing, and lots of algorithms based on deep convolutional neural network (CNN) have been proposed in recent years. Although these algorithms have better accuracy and recovery results than traditional methods without CNN, they ignore finer texture details when super-resolving at a large upscaling factor. To solve this problem, in this paper we propose an algorithm based on generative adversarial network for single-image super-resolution restoration at 4x upscaling factors. We decode a restored high-resolution image by the generative network and make the generator output results finer, more realistic texture details by the adversarial network. We performed experiments on the DIV2K dataset and proved that our method has better performance in single image super-resolution reconstruction. The image quality of this reconstruction method is improved at the peak signal-tonoise ratio and structural similarity index and the results have a good visual effect.

Paper Details

Date Published: 8 February 2019
PDF: 10 pages
Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, 108430J (8 February 2019); doi: 10.1117/12.2505809
Show Author Affiliations
Chunwu Ju, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Science (China)
Xiuqin Su, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Science (China)
Haoyuan Yang, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Science (China)
Hailong Ning, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Science (China)


Published in SPIE Proceedings Vol. 10843:
9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging
Yadong Jiang; Xiaoliang Ma; Xiong Li; Mingbo Pu; Xue Feng; Bernard Kippelen, Editor(s)

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