Frequency Separation Network for Image Super-Resolution

Abstract

It is well-known that high-frequency information (e.g. textures, edges) is significant for single image super-resolution (SISR). However, Existing of deep Convolutional Neural Network (CNN) based methods directly model mapping function from low resolution (LR) to high resolution (HR), and they treat high-frequency and low-frequency information equally during feature extraction. Therefore, the high-frequency learning mode can not be sufficiently attentive, resulting in inaccurate representation of some local details. In this study, we aim to build potential frequencies' relations and handle high-frequency and low-frequency information differentially. Specifically, we propose a novel Frequency Separation Network (FSN) for image super-resolution (SR). In FSN, a new Octave Convolution (OC) is adopted, which uses four operations to perform information update and frequency communication between high frequency and low frequency features. In addition, global and hierarchical feature fusion are employeed to learn elaborate and comprehensive feature representations, in order to further benefit the quality of final image reconstruction. Extensive experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our method.