In a recent study, it was shown that, with adversarial training of an attentive generative network, it is possible to convert a raindrop degraded image into a relatively clean one. However, in real world, raindrop appearance is not only formed by individual raindrops, but also by the distant raindrops accumulation and the atmospheric veiling, namely haze. Current methods are limited in extracting accurate features from a raindrop degraded image with background scene, the blurred raindrop regions, and the haze. In this paper, we propose a new model for an image corrupted by the raindrops and the haze, and introduce an integrated multi-task algorithm to address the joint raindrop and haze removal (JRHR) problem by combining an improved estimate of the atmospheric light, a modified transmission map, a generative adversarial network (GAN) and an optimized visual attention network. The proposed algorithm can extract more accurate features for both sky and non-sky regions. Experimental evaluation has been conducted to show that the proposed algorithm significantly outperforms state-of-the-art algorithms on both synthetic and real-world images in terms of both qualitative and quantitative measures.

中文翻译：

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从单个图像中联合去除雨滴和雾霾


最近的一项研究表明，通过对注意力生成网络进行对抗性训练，可以将雨滴退化图像转换为相对干净的图像。然而，在现实世界中，雨滴的出现不仅是由单个雨滴形成的，而且是由远处雨滴的积累和大气遮蔽（即雾霾）形成的。当前的方法仅限于从具有背景场景、模糊雨滴区域和雾霾的雨滴退化图像中提取准确的特征。在本文中，我们针对雨滴和雾霾损坏的图像提出了一种新模型，并引入了一种集成的多任务算法，通过结合改进的大气光估计来解决联合雨滴和雾霾去除（JRHR）问题，修改后的传输图、生成对抗网络（GAN）和优化的视觉注意网络。所提出的算法可以为天空和非天空区域提取更准确的特征。实验评估表明，在定性和定量测量方面，所提出的算法在合成图像和真实图像上都显着优于最先进的算法。