雨雾天气严重影响了户外拍摄图像质量,针对图像去雾存在的边缘伪影问题,提出了一种基于流形粒子滤波的去雾新方法,通过优化大气透射率,获得精确的透射率,解决了景深边缘伪影问题;针对去雨雾存在雨痕和不清晰问题,提出了一种优化注意生成对抗网络的去雨雾方法,通过将高斯模型与生成对抗网络相结合,去除背景干扰,提高了背景层与雨线分离的准确性,同时在生成对抗网络中加入流形粒子滤波去雾模块,恢复出清晰无雨雾图。采用自然场景雨雾天图像进行测试,并进行定性定量分析比较。实验结果表明,与现存去雨算法相比,所提算法能较好地去除图像中的雨线,且细节特征更加丰富,同时去雾模块的加入显著提高了图像清晰度,客观指标也得到了提升。

Rain and fog weather seriously affects the quality of outdoor images. In this paper, a new method of defogging based on manifold particle filtering is proposed to solve the problem of edge artifacts. By optimizing the atmospheric transmissivity, the accurate transmissivity is obtained, and the problem of edge artifacts in the depth of field is solved. Aiming at rain marks and unclear problems in removing rain and fog, this paper proposes a method that optimizes the attentive generative adversarial network. By combining the Gaussian model with the generative adversarial network, the background interference is removed, and the accuracy of separation of the background layer from the rain line is improved. At the same time, the manifold particle filter fog removal module is added to the generative adversarial network to recover the clear image without rain and fog. The rain and fog images in the natural scene are used for testing, and qualitative and quantitative analyses are conducted. Experimental results show that compared with the existing rain-removal algorithm, the proposed algorithm can remove the rain line in image effectively, and the details are more abundant. At the same time, the addition of the fog removal module significantly improves the image clarity and the objective index.