  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.
