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CASR: a context-aware residual network for single-image super-resolution

  • Deep Learning Approaches for RealTime Image Super Resolution (DLRSR)
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Abstract

With the significant power of deep learning architectures, researchers have made much progress on super-resolution in the past few years. However, due to low representational ability of feature maps extracted from nature scene images, directly applying deep learning architectures for super-resolution could result in poor visual effects. Essentially, unique characteristics like low-frequency information should be emphasized for better shape reconstruction, other than treated equally across different patches and channels. To ease this problem, we propose a lightweight context-aware deep residual network named as CASR network, which appropriately encodes channel and spatial attention information to construct context-aware feature map for single-image super-resolution. We firstly design a task-specified inception block with a novel structure of astrous filters and specially chosen kernel size to extract multi-level information from low-resolution images. Then, a Dual-Attention ResNet module is applied to capture context information by dually connecting spatial and channel attention schemes. With high representational ability of context-aware feature map, CASR can accurately and efficiently generate high-resolution images. Experiments on several popular datasets show the proposed method has achieved better visual improvements and superior efficiencies than most of the existing studies.

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Acknowledgements

This work was supported by National Key R&D Program of China under Grant 2018YFC0407901, the Natural Science Foundation of China under Grant 61702160 and 61602407, the Natural Science Foundation of Jiangsu Province under Grant BK20170892, Natural Science Foundation of Zhejiang Province under Grant LY19F030005 and LY18F020008, and the open Project of the National Key Lab for Novel Software Technology in NJU under Grant K-FKT2017B05.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing , China

    Yirui Wu

  2. National Key Lab for Novel Software Technology, Nanjing University, Nanjing , China

    Yirui Wu & Xiaozhong Ji

  3. School of Natural and Computational Sciences, Massey University, Auckland, New Zealand

    Wanting Ji

  4. Zhejiang Gongshang University, Hangzhou, China

    Yan Tian

  5. School of Engineering, Manukau Institute of Technology, Auckland, New Zealand

    Helen Zhou

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  1. Yirui Wu
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  4. Yan Tian
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  5. Helen Zhou
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Correspondence to Wanting Ji.

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Wu, Y., Ji, X., Ji, W. et al. CASR: a context-aware residual network for single-image super-resolution. Neural Comput & Applic 32, 14533–14548 (2020). https://doi.org/10.1007/s00521-019-04609-8

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