Residual Non-local Attention Networks for Image Restoration

TL;DR

This paper introduces a residual non-local attention network that effectively captures long-range dependencies and adaptively focuses on challenging image regions for improved image restoration quality.

Contribution

It proposes a novel residual non-local attention architecture with local and non-local mask branches, enhancing feature extraction for diverse image restoration tasks.

Findings

Achieves comparable or superior results to state-of-the-art methods

Effectively captures long-range pixel dependencies

Improves restoration quality across various applications

Abstract

In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.