In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

中文翻译：

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无伪像的单图像除雾

在本文中，我们提出了一种名为CurL-Defog的新型除雾技术，目的是在保持良好的对比度恢复和可见性增强的同时，最大程度地减少伪影的插入。许多基于学习的除雾方法都依赖于成对的数据，其中人为地添加了雾以清除图像。这通常在轻微雾化的图像上可提供良好的效果，但在困难的情况下无效。另一方面，使用实际数据训练的模型可以产生视觉上令人印象深刻的结果，但是经常会出现不需要的伪像。我们提出了课程学习策略和增强的CycleGAN模型，以减少人工制品的数量，其中在训练过程中同时使用了合成数据和真实数据。我们还引入了一个新的指标，称为HArD（朦胧工件检测器），在数字上量化去雾图像中伪影的数量，从而避免了对结果的繁琐和主观的人工检查。然后，将HArD与其他除雾指标结合起来，以生成一个不会因伪影的存在而被欺骗的可靠指标。所提出的方法与真实和合成数据集上的最新技术相比均具有优势。