Dust storms may remarkably degrade the imaging quality of Martian orbiters
and delay the progress of mapping the global topography and geomorphology. To
address this issue, this paper presents an approach that reuses the image
dehazing knowledge obtained on Earth to resolve the dust-removal problem on
Mars. In this approach, we collect remote-sensing images captured by Tianwen-1
and manually select hundreds of clean and dusty images. Inspired by the haze
formation process on Earth, we formulate a similar visual degradation process
on clean images and synthesize dusty images sharing a similar feature
distribution with realistic dusty images. These realistic clean and synthetic
dusty image pairs are used to train a deep model that inherently encodes dust
irrelevant features and decodes them into dust-free images. Qualitative and
quantitative results show that dust storms can be effectively eliminated by the
proposed approach, leading to obviously improved topographical and
geomorphological details of Mars.

本文提出了一种利用地球成像除雾知识解决火星上除尘问题的方法，该方法采用天问一号遥感图像，通过手动选择数量众多的清洁和含尘图像，通过训练神经网络，成功地消除了火星上尘暴对图像质量的影响，并提高了其地貌和地形数据的精度。