Abstract
Recently, deep convolutional neural networks have enabled SISR to achieve amazing performance and visual experience. However, we note that most current methods fail to model effective long-distance dependence, either considering only channel-wise attention modeling or considering a self-attention mechanism to model long-range dependencies resulting in a number of parameters and memory costs have increased dramatically, hindering CNNs representation capabilities and deployment on edge devices. To solve this problem, we proposed a novel network named Dual Residual Global Context Attention Network (DRGCAN), which is lightweight and can effectively model global context information. Specifically, we proposed a dual residual in residual structure in which introduce dual residual learning in the residual block of each residual group of the traditional residual in residual (RIR) to fully capture the rich feature information. Furthermore, we further proposed a global context attention mechanism to insert it at the end of each residual group to effectively model long-distance dependence to improve the representation ability of the model. Extensive experiments shown that the proposed model is superior to state-of-the-art SR methods in both visual quality and memory footprint.
This work is supported by Hainan Provincial Natural Science Foundation of China [No. 2019RC018], and by the National Natural Science Foundation of China [61762033], and by the Natural Science Foundation of Hainan [617048, 2018CXTD333] and by Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents.
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Zhou, J. et al. (2020). Dual Residual Global Context Attention Network for Super-Resolution. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_15
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