In this paper we propose a novel end-to-end convolution dehazing architecture, called De-Haze and Smoke CAN (DHSGAN). The model is trained under a generative adversarial network framework to effectively learn the underlying distribution of clean images for the generation of realistic: haze-free images. We train the model on a dataset that is synthesized to include image degradation scenarios from varied conditions of fog, haze, and smoke in both indoor and outdoor settings. Experimental results on both synthetic and natural degraded images demonstrate that our method shows significant robustness over different haze conditions in comparison to the state-of-the-art methods. A group of studies are conducted to evaluate the effectiveness of each module of the proposed method.
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机译：在本文中，我们提出了一种新颖的端到端卷积除雾架构，称为De-Haze and Smoke CAN（DHSGAN）。该模型在生成对抗网络框架下进行训练，可以有效地学习干净图像的基本分布，以生成逼真的无雾图像。我们在综合了数据集的模型上训练模型，该数据集包括室内和室外环境中各种条件下的雾，霾和烟的图像退化情况。在合成和自然退化图像上的实验结果表明，与最新方法相比，我们的方法在不同的雾度条件下均显示出显着的鲁棒性。进行了一组研究，以评估所提出方法的每个模块的有效性。
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