Zheng Chaobing, Yao Yao, Ying Wenjian, Wu Shiqian
Institute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China.
Division of Mechanical Manufacturing and Intelligent Transportation, Beijing Institute of Metrology, Beijing, China.
PLoS One. 2025 Mar 18;20(3):e0315146. doi: 10.1371/journal.pone.0315146. eCollection 2025.
Removing rain streaks from a single image presents a significant challenge due to the spatial variability of the streaks within the rainy image. While data-driven rain removal algorithms have shown promising results, they remain constrained by issues such as heavy reliance on large datasets and limited interpretability. In this paper, we propose a novel approach for single-image de-raining that is guided by Fourier Transform prior knowledge. Our method utilises inherent frequency domain information to efficiently reduce rain streaks and restore image clarity. Initially, the rainy image is decomposed into its amplitude and phase components using the Fourier Transform, where rain streaks predominantly affect the amplitude component. Following this, data-driven algorithms are employed separately to process the amplitude and phase components. Enhanced features are then reconstructed using the inverse Fourier Transform, resulting in improved clarity. Finally, a multi-scale neural network incorporating attention mechanisms at different scales is applied to further refine the processed features, enhancing the robustness of the algorithm. Experimental results demonstrate that our proposed method significantly outperforms existing state-of-the-art approaches, both in qualitative and quantitative evaluations. This innovative strategy effectively combines the strengths of Fourier Transform and data-driven techniques, offering a more interpretable and efficient solution for single-image de-raining (Code: https://github.com/zhengchaobing/DeRain).
由于降雨图像中雨线的空间变异性，从单张图像中去除雨线是一项重大挑战。虽然数据驱动的雨线去除算法已显示出有前景的结果，但它们仍然受到诸如严重依赖大型数据集和有限的可解释性等问题的限制。在本文中，我们提出了一种由傅里叶变换先验知识引导的单图像去雨新方法。我们的方法利用固有的频域信息来有效减少雨线并恢复图像清晰度。首先，使用傅里叶变换将降雨图像分解为其幅度和相位分量，其中雨线主要影响幅度分量。在此之后，分别采用数据驱动算法来处理幅度和相位分量。然后使用逆傅里叶变换重建增强特征，从而提高清晰度。最后，应用一个在不同尺度上包含注意力机制的多尺度神经网络来进一步细化处理后的特征，增强算法的鲁棒性。实验结果表明，我们提出的方法在定性和定量评估中均显著优于现有的最先进方法。这种创新策略有效地结合了傅里叶变换和数据驱动技术的优势，为单图像去雨提供了一种更具可解释性和高效性的解决方案（代码：https://github.com/zhengchaobing/DeRain）。
