Quantitative evaluation for dehazing algorithms on synthetic outdoor hazy dataset

Dehazing is an important image processing technique that has been paid increasing attention in the recent years. But how to quantitatively evaluate the existing dehazing algorithms is still an open issue. In this paper we introduce an effective approach to evaluate the performance of the dehazing algorithms based on a synthetic outdoor dataset. Since it is difficult to simultaneously acquire hazy images and clear reference images, we synthesize haze in real images with complex and multiple scenes, and built an outdoor dataset that contains ground truth reference images, synthetic hazy images, depth maps and transmission maps of the same scene. Due to the fact that the generation of synthetic haze images are based on physical model which is strongly related to the depth information, we propose an effective depth estimation method which combines the geometry and edge information. With our estimated depth map, we are able to create a corresponding hazy scene with high fidelity. Finally, we perform a comprehensive full-reference evaluation of several typical single-image dehazing algorithms on our dataset.