Frequency Reciprocal Action and Fusion for Single Image Super-Resolution

Frequency-based methods have recently received much attention due to their impressive restoration of detail and structure in single image super-resolution (SISR). However, most of these methods mainly use frequency information as auxiliary means but ignore exploring the correlations and pixel distribution differences among various frequencies. To address the limitations, we propose a novel Frequency Reciprocal Action and Fusion Network (FRAF) that explores various frequency correlations and differences. Specifically, we design a Frequency Reciprocal Action (FRA) module, which safely enhances effective spatial information and reduces redundancy by sharing information among various spatial frequencies, to generate refined high- and low-frequency features. These refined frequency features are then progressively to guide the details and structure recovery, respectively. Furthermore, we develop a Detail and Structure Fusion (DSF) module to adaptively select, enhance and fuse the features of these branches to output the final HR image. This way ensures the final image is a high-quality product with rich details and a clear structure. Experimental results demonstrate that our method achieves superior performance over state-of-the-art (SOTA) approaches on both quantitative and qualitative evaluations.