Pyramid Attention Networks for Image Restoration

TL;DR

This paper introduces a Pyramid Attention module that leverages multi-scale features to improve image restoration tasks by capturing long-range correspondences and utilizing coarser scale signals, achieving state-of-the-art results.

Contribution

The paper proposes a novel Pyramid Attention module that effectively exploits multi-scale self-similarities for enhanced image restoration performance.

Findings

State-of-the-art results on multiple image restoration tasks

Effective multi-scale feature correspondence capturing

Flexible integration into various neural architectures

Abstract

Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales. However, recent advanced deep convolutional neural network based methods for image restoration do not take full advantage of self-similarities by relying on self-attention neural modules that only process information at the same scale. To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid. Inspired by the fact that corruptions, such as noise or compression artifacts, drop drastically at coarser image scales, our attention module is designed to be able to borrow clean signals from their "clean" correspondences at the coarser levels. The proposed pyramid attention module is a generic building block that can be flexibly integrated into various neural architectures. Its effectiveness is validated through extensive experiments on multiple image restoration tasks: image denoising, demosaicing, compression artifact reduction, and super resolution. Without any bells and whistles, our PANet (pyramid attention module with simple network backbones) can produce state-of-the-art results with superior accuracy and visual quality. Our code will be available at https://github.com/SHI-Labs/Pyramid-Attention-Networks