液晶与显示2024，Vol.39Issue(7)：950-960,11.DOI:10.37188/CJLCD.2023-0227
基于拆分注意力网络的单图像超分辨率重建
Single image super-resolution reconstruction based on split-attention networks
摘要
Abstract
A single image super-resolution reconstruction method for splitting attention networks is proposed to address the problems of lack of texture details,insufficient feature extraction,and unstable training in the existing generation of adversarial networks under large-scale factors.Firstly,the generator is constructed using the split attention residual module as the basic residual block,which improves the generator's feature extraction ability.Secondly,Charbonnier loss function with better robustness and focal frequency loss are introduced into the loss function to replace the mean square error loss function,and regularization loss smoothing training results are added to prevent the image from being too pixelated.Finally,spectral normalization is used in both the generator and discriminator to improve the stability of the network.Compared with other methods tested on Set5,Set14,Urban100 and BSDS100 test sets at a magnification factor of 4,the peak signal-to-noise ratio of this method is 1.419 dB higher than the average value of other comparison methods in this article,and the structural similarity is 0.051 higher than the average value.Experimental data and renderings indicate that this method subjectively has rich details and better visual effects,while objectively has high peak signal-to-noise ratio and structural similarity values.关键词
超分辨率/生成对抗网络/谱归一化/拆分注意力网络Key words
super resolution/generative adversarial network/spectral normalization/split-attention networks分类
信息技术与安全科学引用本文复制引用
彭晏飞,刘蓝兮,王刚,孟欣,李泳欣..基于拆分注意力网络的单图像超分辨率重建[J].液晶与显示,2024,39(7):950-960,11.基金项目
国家自然科学基金(No.61772249) （No.61772249）
辽宁省高等学校基本科研项目(No.LJKZ0358)Supported by National Natural Science Foundation of China(No.61772249) （No.LJKZ0358）
Basic Research Project of Colleges and Universities in Liaoning Province(No.LJKZ0358) （No.LJKZ0358）
