Non-Local Hierarchical Residual Network for Single Image Super-Resolution
Furui Bai, Wen Bing Lu, Lin Zha, Xiaopeng Sun, Ruoxuan Guan · 2019
Recently, deep convolutional neural networks (CNNs) have been demonstrated excellent performance on single image super-resolution (SISR). However, most deep learning based methods lack the ability to distinguish features in network. For image super-resolution, it is important to design an effective prior to learn the correlation of various feature. To solve this problem, we propose a non-local hierarchical residual network (NHRN) for SISR. Specifically, we introduce a non-local module to measure the self-similarity between each pixels in the feature map, and obtain a weight matrix guiding the deep network to find more precise relationship between L-R and HR images. Thus our method reconstruct images with a sharper edge. In addition, we employ group convolutions to build a hierarchical residual structure, which enable the network extract image features hierarchically. It can reduce the executive time while ensuring reconstruct image quality. Extensive experiments show that our NHRN achieves better accuracy and speed against state-of-the-art methods.