Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Yulun Zhang; Kunpeng Li; Kai Li; Lichen Wang; Bineng Zhong; Yun Fu

doi:10.1007/978-3-030-01234-2_18

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Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Yulun Zhang Kunpeng Li Kai Li Lichen Wang Bineng Zhong Yun Fu

Year: 2018 Lecture notes in computer science Pages: 294-310 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-01234-2_18

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Keywords:

Residual Computer science Convolutional neural network Channel (broadcasting) Focus (optics) Artificial intelligence Deep learning Image (mathematics) Pattern recognition (psychology) Algorithm Telecommunications Optics

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Topics

Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Processing Techniques and Applications

Physical Sciences → Engineering → Media Technology

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

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