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.
翻译:多图像超分辨率旨在从同一地点的多个图像中结合并恢复高分辨率图像,对于利用卫星图像至关重要,卫星图像往往被云层等大气扰动所包围,扰动的位置因图像而异,建议采用许多辐射计和几何方法探测大气扰动,但利用探测结果,即深层学习的高质量地图仅限于预处理或计算损失。在本文件中,我们提出了一个高质量的地图相关关注网络(QA-Net),这是一个首次将QMS充分纳入深层学习计划的架构。我们提议的注意模块在低分辨率图像的同时处理QMS,并利用QM特性来区分扰动和关注图像特征。因此,QA-Net在PROBA-V数据集中取得了最新成果。