Single image fast dehazing based on haze density classification prior

Most of existing the dehazing methods have high computational complexity and poor dehazing quality. Therefore, a fast haze removal method based on the haze density classification prior is proposed. Haze density is reflected by the difference between maximum channel and minimum channel. Based on this, haze density prior (HDP) is proposed. The HDP can effectively distinction between mist image and dense haze image, and quickly estimate the atmospheric light veil. In addition, an optimized method for estimating atmospheric light using the mid-channel of haze image is proposed, which overcomes limitations of global atmospheric light. Our method can fast and efficiently recover clear image because there is no estimation of transmission map. Experiments show that the proposed method outperforms existing methods in haze removal, especially for dense haze images. Comparison of objective evaluation and running time shows that the proposed method is effective and real-time.

Automated summary

This study proposes a fast haze removal method based on the haze density classification prior, which can accurately distinguish between mist image and dense haze image, and effectively estimate the atmospheric light veil. The method utilizes the difference between maximum channel and minimum channel to reflect haze density, and uses the mid-channel of haze image for atmospheric light estimation. By eliminating the need for transmission map estimation, the proposed method can quickly and efficiently recover clear image.