论文标题
深度学习消除了田文-1的图像中的大规模沙尘暴
Deep Learning Eliminates Massive Dust Storms from Images of Tianwen-1
论文作者
论文摘要
沙尘暴可能会显着降低火星轨道轨道轨道的成像质量，并延迟绘制全球地形和地貌学的进度。为了解决这个问题，本文提出了一种方法，该方法可以重用地球上获得的图像去除图像，以解决火星上的灰尘回避问题。在这种方法中，我们收集了Tianwen-1捕获的遥感图像，并手动选择了数百个干净和灰尘的图像。受到地球上雾兹的形成过程的启发，我们在干净的图像上制定了类似的视觉降解过程，并合成了与逼真的尘土飞扬图像共享类似特征分布的尘土图像。这些逼真的清洁和合成的尘土图像对用于训练一个固有地编码灰尘无关的特征并将它们解码为无尘图像的深层模型。定性和定量结果表明，拟议方法可以有效地消除沙尘暴，从而显然改善了火星的地形和地貌细节。
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.
