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.
作者
陈健
HUANG Detian
HUANG Weiqin
CHEN Jian;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)
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)。