Novel Defogging Algorithm Based on the Joint Use of Saturation and Color Attenuation Prior

Abstract

Focusing on the defects of famous defogging algorithms for fog images based on the atmosphere scattering model, we find that it is necessary to obtain accurate transmission map that can reflect the real depths both in large depth and close range. And it is hard to tackle this with just one prior because of the differences between the large depth and close range in foggy images. Hence, we propose a novel prior that simplifies the solution of transmission map by transferring coefficient, called saturation prior. Then, under the Random Walk model, we constrain the transferring coefficient with the color attenuation prior that can obtain good transmission map in large depth regions. More importantly, we design a regularization weight to balance the influences of saturation prior and color attenuation prior to the transferring coefficient. Experimental results demonstrate that the proposed defogging method outperforms the state-of-art image defogging methods based on single prior in terms of details restoring and color preserving.

Research progress and applications of image defogging algorithms

“…Early defogging algorithms aimed to enhance the global contrast of images and highlight their details to achieve the defogging effect, such as histogram equalization, Retinex algorithm, and wavelet transform [5][6][7][8][9][10]. With the proposal of atmospheric scattering models, researchers have established the mapping relationship between hazy images and haze-free images by analyzing atmospheric light scattering and attenuation, and reversed this relationship to obtain dehazed images [11][12][13][14]. There are also defogging algorithms based on neural networks.…”