Multi-image Super-resolution via Quality Map Associated Attention Network
This addresses the challenge of enhancing satellite image resolution for remote sensing applications, but it is incremental as it builds on existing quality map detection methods.
The paper tackled the problem of multi-image super-resolution for satellite images occluded by atmospheric disturbances like clouds, by proposing a quality map-associated attention network (QA-Net) that fully incorporates quality maps into deep learning, achieving state-of-the-art results on the PROBA-V dataset.
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.