Learning global attention-gated multi-scale memory residual networks for single-image super-resolution

Jing Wang; Huihui Song; Kaihua Zhang; Qingshan Liu

doi:10.11834/jig.200174

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Learning global attention-gated multi-scale memory residual networks for single-image super-resolution

Jing Wang Huihui Song Kaihua Zhang Qingshan Liu

Year: 2021 Journal: Journal of Image and Graphics Vol: 26 (4)Pages: 766-775 Publisher: University of Portsmouth

DOI: 10.11834/jig.200174

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Keywords:

Residual Computer science Superresolution Scale (ratio) Artificial intelligence Resolution (logic) Image (mathematics) Pattern recognition (psychology) Algorithm Cartography

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Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Photoacoustic and Ultrasonic Imaging

Physical Sciences → Engineering → Biomedical Engineering

Advanced Image Fusion Techniques

Physical Sciences → Engineering → Media Technology

Learning global attention-gated multi-scale memory residual networks for single-image super-resolution

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