Quantitative Performance Evaluation for Dehazing Algorithms on Synthetic Outdoor Hazy Images

Dehazing has emerged as a promising technology to recover the clear image from an input hazy image, such that the image quality can be significantly enhanced. However, it is still an open issue to quantitatively evaluate the performance of existing dehazing algorithms. To fill in this gap, we propose an effective performance evaluation method for image dehazing by exploiting synthetic outdoor hazy images dataset. To be specific, we first synthesize hazes on the original haze-free images to approximate the real-world hazy scenes. A dataset that contains original images, synthetic hazy images, estimated depth maps, and transmission maps of the same scene is then established. Due to the fact that the generation of the synthetic hazy images is based on the physical model which is strongly related to the depth information, an effective depth estimation method that combines the geometry and edge information is proposed. With the estimated depth map, we are able to create a corresponding hazy scene with high fidelity. Numerous experiments are conducted to verify the consistency of subjective and objective evaluation of the proposed method.