A lightweight multi-scale residual network for single image super-resolution
Author:
Funder
Shanxi Scholarship Council of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-022-02136-z.pdf
Reference35 articles.
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3. Gu, J., Lu, H., Zuo, W.M., Dong, C.: Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1604–1613 (2019)
4. Li, B., Liu, R., Cao, J., Zhang, J., Lai, Y.-K.: Liu, X. Online low-rank representation learning for joint multi-subspace recovery and clustering. IEEE Trans. Image Process. 27(1), 335–348 (2018)
5. Huang, J.-B, Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 5197–5206 (2015)
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