Self-calibrated Attention Residual Network for Image Super-Resolution

Abstract

Deep Convolutional Neural Networks (DCNNs) have achieved remarkable performance in single image super-resolution (SISR). However, most SR methods restore high resolution (HR) images from single-scale region in the low resolution (LR) input, which limits the ability of method to infer multi-scales of details for high resolution (HR) output. In this paper a novel basic building block called self-Calibrated residual block (SARB) is proposed to solve this problem. SARB consists of carefully designed multi-scale paths, which can capture rich structure information from different scale. In addition, self-Calibrated residual block is introduced to adaptively learn informatively context to make network generate more discriminative representations. These blocks are composed of self-calibrated attention residual network (SARN) for image super-resolution. Experiments results on five benchmark datasets demonstrate that the proposed SARN achieves comparable results compared with the previous most of the state-of-the-art methods.