Multi-image Super-resolution via Quality Map Associated Attention Network

TL;DR

This paper introduces QA-Net, a novel deep learning architecture that integrates quality maps into the attention mechanism for multi-image super-resolution, significantly improving satellite image restoration under atmospheric disturbances.

Contribution

The paper presents the first deep learning model that fully incorporates quality maps into the attention modules for super-resolution tasks.

Findings

Achieved state-of-the-art results on the PROBA-V dataset.

Effectively distinguishes atmospheric disturbances using quality maps.

Improves super-resolution quality in occluded satellite images.

Abstract

Multi-image super-resolution, which aims to fuse and restore a high-resolution image from multiple images at the same location, is crucial for utilizing satellite images. The satellite images are often occluded by atmospheric disturbances such as clouds, and the position of the disturbances varies by the images. Many radiometric and geometric approaches are proposed to detect atmospheric disturbances. Still, the utilization of detection results, i.e., quality maps in deep learning was limited to pre-processing or computation of loss. In this paper, we present a quality map-associated attention network (QA-Net), an architecture that fully incorporates QMs into a deep learning scheme for the first time. Our proposed attention modules process QMs alongside the low-resolution images and utilize the QM features to distinguish the disturbances and attend to image features. As a result, QA-Net has achieved state-of-the-art results in the PROBA-V dataset.