Contrastive Attention-guided Multi-level Feature Registration for Reference-based Super-resolution

Given low-quality input and assisted by referential images, reference-based super-resolution (RefSR) strives to enlarge the spatial size with the guarantee of realistic textures, for which sophisticated feature-matching strategies are naturally demanded. However, the miserable transformation gap between inputs and references, e.g., texture rotation and scaling within patches, often yields distorted textures and terrible ghosting artifacts, which seriously hampers the visual senses and their further investigation. To circumvent this challenge, we propose a contrastive attention-guided multi-level feature registration for RefSR, explicitly tapping the potential of interacting between inputs and references. Specifically, we develop a multi-level feature warping scheme, involving patch-level coarse feature swapping and pixel-level deformable alignment, to model generalized spatial transformation correspondences steered by contrastive attention. Notably, a spatial registration module is embedded for further calibration against the potential misalignment issue and inter-feature distribution difference. In addition, aiming at suppressing the impacts of irrelevant or superfluous information on cross-scale features, we incorporate a multi-residual feature fusion module to strive for visually plausible textures. Experimental results on four publicly available datasets demonstrate that our method outperforms most state-of-the-art approaches in terms of both efficiency and perceptual effectiveness.