Single-image super-resolution via a lightweight convolutional neural network with improved shuffle learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02730-9.pdf
Reference41 articles.
1. Chen, X., Yang, R., Guo, C.: A lightweight multi-scale residual network for single image super-resolution. SIViP 16, 1793–1801 (2022). https://doi.org/10.1007/s11760-022-02136-z
2. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2), 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.2439281
3. Dong, C., Loy, C. C., Tang, X.: Accelerating the Super-Resolution Convolutional Neural Network In: European Conference on Computer Vision, pp. 391–407 (2016)
4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https://doi.org/10.48550/arXiv.1512.03385.
5. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646–1654 (2016)
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