Adaptive feature denoising based deep convolutional network for single image super-resolution

Abstract

Recently, the feature map recalibration (FMR) mechanism has been widely explored in single image super-resolution (SISR) and obtained remarkable performances. However, the existing FMR-based SISR methods directly incorporate the attention module into a deeper network structure (e.g. EDSR), while neglecting the differences between the low-level and high-level vision problems. In this paper, we design a low-level specific FMR mechanism for SISR task based on a new observation by examining current SISR methods, which all demonstrate a solid correlation between the SISR performance and the convolutional feature noise. Inspired by this, we extend the classic soft thresholding technique in the way of deep network, and develop an Adaptive Soft Thresholding (AST) module for feature noise suppression. Comparing to existing attention modules, AST is light-weighted and can be taken as an easy plug-in module in any SISR networks. To this end, we construct a adaptive Feature Denoising Super-Resolution (FDSR) network by combining the baseline EDSR and the proposed AST. Extensive experimental results show that the proposed FDSR network could achieve the state-of-the-art performances on SISR benchmarks, and significantly reduce the parameter (28.8% for EDSR, 74.6% for RCAN,s 76.0% for SAN) with respect to FMR module.