Channel attention based wavelet cascaded network for image super-resolution  

作　　者：CHEN Jian HUANG Detian HUANG Weiqin 陈健;HUANG Detian;HUANG Weiqin(College of Engineering,Huaqiao University,Quanzhou 362021,P.R.China;School of Information Science and Technology,Xiamen University Tan Kah Kee College,Zhangzhou 363105,P R.China)

机构地区：[1]College of Engineering,Huaqiao University,Quanzhou 362021,P.R.China [2]School of Information Science and Technology,Xiamen University Tan Kah Kee College,Zhangzhou 363105,P R.China

出　　处：《High Technology Letters》2022年第2期197-207,共11页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61901183);Fundamental Research Funds for the Central Universities(No.ZQN921);Natural Science Foundation of Fujian Province Science and Technology Department(No.2021H6037);Key Project of Quanzhou Science and Technology Plan(No.2021C008R);Natural Science Foundation of Fujian Province(No.2019J01010561);Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province 2019(No.JAT191080);Science and Technology Bureau of Quanzhou(No.2017G046)。

摘　　要：Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.

关 键 词：image super-resolution(SR) wavelet transform convolutional neural network(CNN) second-order channel attention(SOCA) non-local self-similarity 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]