Shi Fei, Jia Zhenhong, Zhou Yanyun
School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China.
Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China.
Sensors (Basel). 2025 Mar 18;25(6):1889. doi: 10.3390/s25061889.
Natural sand-dust weather is complicated, and synthetic sand-dust datasets cannot accurately reflect the properties of real sand-dust images. Sand-dust image enhancement and restoration methods that are based on enhancement, on priors, or on data-driven may not perform well in some scenes. Therefore, it is important to develop a robust sand-dust image restoration method to improve the information processing ability of computer vision. In this paper, we propose a new zero-shot learning method based on an atmospheric scattering physics model to restore sand-dust images. The technique has two advantages: First, as it is unsupervised, the model can be trained without any prior knowledge or image pairs. Second, the method obtains transmission and atmospheric light by learning and inferring from a single real sand-dust image. Extensive experiments are performed and evaluated both qualitatively and quantitatively. The results show that the proposed method works better than the state-of-the-art algorithms for enhancing and restoring sand-dust images.
自然沙尘天气情况复杂，合成沙尘数据集无法准确反映真实沙尘图像的特性。基于增强、先验或数据驱动的沙尘图像增强和恢复方法在某些场景中可能效果不佳。因此，开发一种强大的沙尘图像恢复方法以提高计算机视觉的信息处理能力至关重要。在本文中，我们提出了一种基于大气散射物理模型的新型零样本学习方法来恢复沙尘图像。该技术有两个优点：第一，由于它是无监督的，模型可以在没有任何先验知识或图像对的情况下进行训练。第二，该方法通过从单个真实沙尘图像中学习和推断来获得透射率和大气光。我们进行了大量实验，并进行了定性和定量评估。结果表明，所提出的方法在增强和恢复沙尘图像方面比现有最先进算法表现更好。
