https://scholars.lib.ntu.edu.tw/handle/123456789/638631
標題: | LSR: A Light-Weight Super-Resolution Method | 作者: | Wang, Wei Lei, Xuejing Chen, Yueru MING-SUI LEE Kuo, C. C.Jay |
關鍵字: | Green Learning | Mobile Computing | Super-resolution | 公開日期: | 1-一月-2023 | 來源出版物: | Proceedings - International Conference on Image Processing, ICIP | 摘要: | A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work. LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. To lower the computational complexity, LSR does not adopt the end-to-end optimization deep networks. It consists of three modules: 1) generation of a pool of rich and diversified representations in the neighborhood of a target pixel via unsupervised learning, 2) selecting a subset from the representation pool that is most relevant to the underlying super-resolution task automatically via supervised learning, 3) predicting the residual of the target pixel via regression. LSR has low computational complexity and reasonable model size so that it can be implemented on mobile/edge platforms conveniently. Besides, it offers better visual quality than classical exemplar-based methods in terms of PSNR/SSIM measures. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/638631 | ISBN: | 9781728198354 | ISSN: | 15224880 | DOI: | 10.1109/ICIP49359.2023.10222337 |
顯示於: | 資訊工程學系 |
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