LSR: A Light-Weight Super-Resolution Method

• URL: http://arxiv.org/abs/2302.13596v1
• Date: Mon, 27 Feb 2023 09:02:35 GMT
• Title: LSR: A Light-Weight Super-Resolution Method
• Authors: Wei Wang, Xuejing Lei, Yueru Chen, Ming-Sui Lee, C.-C. Jay Kuo
• Abstract summary: LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. 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.
• Score: 36.14816868964436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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