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http://dx.doi.org/10.15207/JKCS.2020.11.12.015

Deep Learning-based Super Resolution Method Using Combination of Channel Attention and Spatial Attention  

Lee, Dong-Woo (Dept. of Plasma Bio Display, KwangWoon University)
Lee, Sang-Hun (Ingenium College of Liberal Arts, KwangWoon University)
Han, Hyun Ho (College of General Education, University of Ulsan)
Publication Information
Journal of the Korea Convergence Society / v.11, no.12, 2020 , pp. 15-22 More about this Journal
Abstract
In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.
Keywords
Super Resolution; CNN; Residual Block; Channel Attention; Spatial Attention;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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