High-to-low-level feature matching and complementary information fusion for reference-based image super-resolution

The aim of the reference-based image super-resolution (RefSR) is to reconstruct high-resolution (HR) when a reference (Ref) image with similar content as that of the low-resolution (LR) input is given. In the task, the quality of existing approaches degrades severely when there are several similar objects but different contents. Besides, not all similar information in the reference image is useful for the input image. Therefore, we propose high-to-low-level feature matching and complementary information fusion (HMCF) network for RefSR. The matching strategy adopts high-level to low-level feature matching to distinguish similar objects but different contents according to high-level semantics. The complementary information fusion module utilizes the channel and spatial attention to select the complement information for LR image and keeps the pixel consistency of input and Ref image. We perform extensive experiments to demonstrate that our proposed HMCF obtains the SOTA performance on the RefSR benchmarks and presents a high visual quality.