Enhanced Residual Fourier Transformation Network for Lightweight Image Super-resolution

Abstract

Residual blocks have been widely used in the single image super-resolution (SR), boosting the performance of SR results. The stacking of residual blocks helps to grab more information needed for the recovery of high-resolution (HR) images, but at the cost of larger model size and computational complexity. To address this problem, we propose a lightweight enhanced residual fourier transformation network (ERFTN). To ensure the network is lightweight enough while being capable of capturing both spatial and frequency information, we introduce a novel enhanced residual fourier transformation block (ERFTB) with an enhanced spatial attention (ESA) block. In addition, we adopt the contrastive loss for training acceleration without additional parameters. Extensive experiments demonstrate that our method can achieve SR performance superior to the state-of-the-art SR methods while reducing approximately half of the number of parameters (e.g., with 190K number of parameters, achieving 32.39dB in PSNR on Urban100 dataset).