Residual scale attention network for arbitrary scale image super-resolution-Reference-Cited by-同舟云学术

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Residual scale attention network for arbitrary scale image super-resolution

Published:2021-02 Issue: Volume:427 Page:201-211
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Fu Ying,Chen Jian,Zhang Tao,Lin Yonggang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference41 articles.

1. Learning a deep convolutional network for image super-resolution;Dong;Proceedings of the European Conference on Computer Vision,2014

2. Camera lens super-resolution;Chen,2019

3. Zoom to learn, learn to zoom;Zhang,2019

4. Toward real-world single image super-resolution: a new benchmark and a new model;Cai,2019

5. Meta-sr: a magnification-arbitrary network for super-resolution;Hu,2019

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1. Research on image super-resolution reconstruction algorithms based on deep learning;International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024);2024-06-13

2. Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey;Information Fusion;2024-02

3. Arbitrary-Scale Image Super-Resolution via Degradation Perception;IEEE Transactions on Computational Imaging;2024

4. Activating More Information in Arbitrary-Scale Image Super-Resolution;IEEE Transactions on Multimedia;2024

5. An efficient multi-scale learning method for image super-resolution networks;Neural Networks;2024-01

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