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Cited by 2,415

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Image Super-Resolution via Deep Recursive Residual Network

Tai, Ying, Yang, Jian, Liu, Xiaoming·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2017.298
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Abstract

Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks. Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks, recursive learning is used to control the model parameters while increasing the depth. Extensive benchmark evaluation shows that DRRN significantly outperforms state of the art in SISR, while utilizing far fewer parameters. Code is available at https://github.com/tyshiwo/DRRN_CVPR17.

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Metadata

Authors
Tai, Y., Yang, J., & Liu, X.
Venue
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2017-07
Pages
2790-2798
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2017.298
OpenAlex
W2747898905
Cited by
2,415
References
9
FWCI
66.17
Type
conference paper
Language
en
Open access
closed

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