Satellite cloud images play an important role in weather analysis and forecast. High-resolution satellite images play a significant role in the study of mesoscale weather systems such as typhoons. With the increasing demands of locating and tracking techniques, the resolution of satellite images is no longer satisfactory. Enhancing their resolution with superresolution (SR) methods can help in identifying and locating weather systems. In this paper, we propose a multipath network model, called SRCloudNet, that involves joint training of a back-projection network and a local residual network. SRCloudNet integrates features extracted from back-projection units and residual dense blocks to achieve more accurate image reconstruction. We also developed a novel natural-color cloud and contrail image data set, constituting the first-ever satellite cloud image data set established for SR research. Because of the special features, contrail images were first used to test the performance of SRCloudNet. Extensive experiments demonstrated that SRCloudNet achieves superior performance.

中文翻译：

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用于重建卫星云图像的深度多径网络超分辨率成像

卫星云图在天气分析和预报中发挥着重要作用。高分辨率卫星图像在台风等中尺度天气系统的研究中发挥着重要作用。随着定位和跟踪技术需求的不断增加，卫星图像的分辨率已不再令人满意。使用超分辨率 (SR) 方法提高分辨率有助于识别和定位天气系统。在本文中，我们提出了一种称为 SRCloudNet 的多路径网络模型，它涉及反投影网络和局部残差网络的联合训练。SRCloudNet 集成了从反投影单元和残差密集块中提取的特征，以实现更准确的图像重建。我们还开发了一种新颖的自然色云和轨迹图像数据集，构成有史以来第一个为 SR 研究建立的卫星云图像数据集。由于其特殊性，首先使用轨迹图像来测试 SRCloudNet 的性能。大量实验表明，SRCloudNet 实现了卓越的性能。