Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

version 1.0.0.0 (93.6 MB) by Kai Zhang
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

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Updated 9 Oct 2021

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Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018)
In contrast to other CNN-based SISR methods which only take the LR image as input and lack scalability to handle other degradations, the proposed network takes the concatenated LR image and degradation maps as input, thus allowing a single model to manipulate multiple and even spatially variant degradations.
@inproceedings{zhang2018learning,
title={Learning a Single Convolutional Super-Resolution Network for Multiple Degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2018},
}

Cite As

Kai Zhang (2022). Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (https://github.com/cszn/SRMD), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2015b
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