Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism

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Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism

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[1]BO Yangyu,WU Yongliang,WANG Xuejun.Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):48-55.[doi:10.13705/j.issn.1671-6833.2024.03.009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 48-55 Column: Public date: 2024-09-25

Title:: Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism

Author(s):: BO Yangyu; WU Yongliang; WANG Xuejun; College of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Keywords:: image super-resolution reconstruction; local spatial attention; residual fusion attention; atrous pyramid; double feature extraction

CLC:: TP751 TP391. 41 TP183

DOI:: 10.13705/j.issn.1671-6833.2024.03.009

Abstract:: In the process of image super-resolution reconstruction, high frequency features might be ignored, whichwould lead to insufficient extraction features and fuzzy texture details in the reconstructed image. To solve this problem, an image super-resolution reconstruction network based on double feature extraction and attention mechanismwas proposed. In particular, in this study, a two-branch network for feature extraction was proposed to solve theproblem that high frequency features and multi-scale features could not be effectively extracted and uniformly fusedduring image reconstruction. In addition, in order to make the network obtain more accurate high-frequency features, a local spatial attention module was proposed, and combined with channel attention. A residual fusion attention module was constructed to improve the network′s ability to locate high-frequency features. Finally, the atrouspyramid module was designed to enlarge the receptive field of the network and enable the multi-scale feature extraction. Experiments were carried out on four benchmark datasets, and the results were better than the current advanced methods. Especially when the super-resolution multiple was 4, the proposed method improved the optimalPSNR by 0. 16, 0. 08, 0. 03 and 0. 20 dB, respectively, compared with the current mainstream models. The experimental results shown that the proposed method achieved better improvement in visual effect and quantitative analysis

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Last Update: 2024-09-29