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 that misses 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.

本研究旨在解决单张图像超分辨率任务中，通道注意力对不同图层之间相关性的忽视问题，为此，提出了一种新的整体注意力网络（HAN），其中包括层注意力模块（LAM）和通道-空间注意力模块（CSAM），它们旨在模拟层、通道、位置的整体依赖关系，以更好地保留信息丰富的特征，并通过广泛实验，取得了较好的效果。

基于整体注意力网络的单幅图像超分辨率