Super-resolution reconstruction algorithm based on deep learning mechanism and wavelet fusion

被引：0

作者：

Yang S. ^{[1
]}

Wang H. ^{[2
]}

Wang Y. ^{[1
]}

Li J. ^{[2
]}

Wang Y. ^{[1
]}

机构：

[1] School of Information, North China University of Technology, Beijing

[2] School of Software, Beihang University, Beijing

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 01期

关键词：

Deep learning; Generative adversarial network (GAN); Mult-iresolution analysis; Super-resolution reconstruction; Wavelet transform;

D O I：

10.13700/j.bh.1001-5965.2019.0146

中图分类号：

学科分类号：

摘要：

Deep learning technology has developed rapidly in the field of super-resolution reconstruction. In order to further improve the quality and visual effect of reconstructed images, this paper proposes a super-resolution reconstruction based on wavelet transform and generative adversarial networks (GAN) for the unnatural problem of texture reconstruction based on the super-resolution reconstruction algorithm of GAN. In this paper, each component of the wavelet decomposition in the GAN is trained in separate subnets to realize the prediction of wavelet coefficients by the network. Effectively reconstruct high-resolution images with rich global information and local texture details. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity of the objective evaluation index of the reconstructed image can be improved by at least 0.99 dB and 0.031, respectively, based on the algorithm of GAN. © 2020, Editorial Board of JBUAA. All right reserved.

引用

页码：189 / 197

页数：8

共 34 条

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