Cycle-GAN Network Incorporated With Atmospheric Scattering Model for Dust Removal of Martian Optical Images

作者：Hongxia Ye, Haiyue Xiang, Feng Xu · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3432601 · 被引用次数：5 · 研究领域：Planetary Science and Exploration

Dust particles in Martian atmosphere can significantly reduce visibility. This article proposes a physically guided neural network approach for dust removal of Martian images by incorporating atmospheric scattering model into the cycle-consistent generative adversarial network (Cycle-GAN) framework. The network consists of two primary modules: dust removal and dust addition, both of which combine neural networks and physical models. The dust-removal process estimates the scattering coefficient and transmission map and then physically implements dust removal with the atmospheric scattering model. The dust-addition process estimates the scene depth, which is then combined with the atmospheric scattering coefficients obtained by the dust-removal process to compute the transmission map. The transmission map is substituted into the atmospheric scattering model for dust addition of clean images. Moreover, the additional loss of transmission map and scattering coefficient furtherly enhances the consistency constraints. Martian clear and dust images collected by the Mars Curiosity rover are used to train and evaluate the new dust-removal approach. Extensive ablation experiments demonstrate the effectiveness of incorporating the physical model and the additional loss functions. Furthermore, the scattering coefficients learned by the network are validated with the Mie scattering theory, ensuring the physical plausibility of the estimated parameters. The key advantage of this approach i...