Single Image Super-Resolution via a Holistic Attention Network
Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao
Abstract
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process, which is kind of missing the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM), to model the holistic interdependencies among layers, channels, and positions. Specifically, the proposed LAM adaptively emphasizes hierarchical features by considering correlations among layers. Meanwhile, CSAM learns the confidence at all the positions of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super-resolution approaches."
BibTeX
@inproceedings{eccv2020_singleimagesuper,
title = {Single Image Super-Resolution via a Holistic Attention Network},
author = {Ben Niu and Weilei Wen and Wenqi Ren and Xiangde Zhang and Lianping Yang and Shuzhen Wang and Kaihao Zhang and Xiaochun Cao and Haifeng Shen},
booktitle = {ECCV 2020},
year = {2020}
}