RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection

• URL: http://arxiv.org/abs/2211.04203v1
• Date: Tue, 8 Nov 2022 12:39:35 GMT
• Title: RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection
• Authors: Lin Zhang, Xin Li, Dongliang He, Fu Li, Yili Wang, Zhaoxiang Zhang
• Abstract summary: We propose a reciprocal learning framework to reinforce the learning of a RefSR network. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm.
• Score: 66.08293086254851
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as. Therefore, in this work, we propose a reciprocal learning framework that can appropriately leverage such a fact to reinforce the learning of a RefSR network. Besides, we deliberately design a progressive feature alignment and selection module for further improving the RefSR task. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection in a progressive manner, thus more precise reference features can be transferred into the input features and the network capability is enhanced. Our reciprocal learning paradigm is model-agnostic and it can be applied to arbitrary RefSR models. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm. Furthermore, our proposed model together with the reciprocal learning strategy sets new state-of-the-art performances on multiple benchmarks.

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