Name
Title | ||
|---|---|---|
RRSR: Reciprocal Reference-Based Image Super-Resolution with Progressive Feature Alignment and Selection. |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-19800-7_38 | European Conference on Computer Vision |
Keywords | DocType | Citations |
Reference-based image super-resolution,Reciprocal learning,Reference-input feature alignment | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |