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

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