论文标题
通过质量图相关注意网络的多图像超分辨率
Multi-image Super-resolution via Quality Map Associated Attention Network
论文作者
论文摘要
旨在从同一位置的多个图像融合和恢复高分辨率图像的多图像超分辨率对于利用卫星图像至关重要。卫星图像通常被大气干扰（例如云）遮住，而干扰的位置随图像而变化。提出了许多辐射方法和几何方法来检测大气干扰。尽管如此，对检测结果的利用，即深度学习中的质量图仅限于预处理或计算损失。在本文中，我们提出了与地图相关的质量相关的注意网络（QA-NET），该网络首次将QM完全融入深度学习方案中。我们提出的注意力模块与低分辨率图像一起处理QMS，并利用QM功能来区分干扰并注意图像功能。结果，QA-NET在Proba-V数据集中实现了最先进的结果。
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.
