Jingjun Zhou1,2, Jing Liu3, Jingbing Li1,2,*, Mengxing Huang1,2, Jieren Cheng4, Yen-Wei Chen5, Yingying Xu3,6, Saqib Ali Nawaz1
Computer Systems Science and Engineering, Vol.39, No.1, pp. 133-146, 2021, DOI:10.32604/csse.2021.016633
Abstract Recent applications of convolutional neural networks (CNNs) in single image super-resolution (SISR) have achieved unprecedented performance. However, existing CNN-based SISR network structure design consider mostly only channel or spatial information, and cannot make full use of both channel and spatial information to improve SISR performance further. The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information. Specifically, we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network. This structure… More >
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