Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a hazy image is synthesized based on a clean image and its estimated depth map. This paradigm, however, can produce low-quality hazy images due to inaccurate depth estimation, resulting in poor generalization of the trained models. In this paper, we explore an alternative approach for generating paired clean-hazy images by leveraging computer graphics. Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models. To this end, we present SimHaze: a new synthetic haze dataset. More importantly, we show that training with SimHaze alone allows the latest dehazing models to achieve significantly better performance in comparison to previous dehazing datasets. Our dataset and code will be made publicly available.

翻译：深度模型在单图像去雾领域已取得显著成功。现有方法大多采用全监督训练方式，通过配对的无雾与有雾图像进行学习，其中有雾图像根据无雾图像及其估计的深度图合成生成。然而，由于深度估计不准确，这种范式生成的有雾图像质量低下，导致训练模型泛化性能较差。本文探索了一种利用计算机图形学生成配对无雾-有雾图像的替代方案。通过采用现代游戏引擎，我们的方法能够渲染出清晰的无雾图像及其精确的深度图，并基于这些数据合成高质量有雾图像以训练去雾模型。为此，我们提出新型合成有雾数据集SimHaze。更重要的是，实验表明，仅使用SimHaze进行训练，就能使最新去雾模型的性能显著优于基于以往数据集的训练结果。该数据集及代码将公开发布。