Improvement of dehazing algorithm based on dark channel priori theory

Abstract The dehazing algorithm based on dark channel priori theory is an effective method for dehazing of single image. However, it still has shortcomings in some of special cases. In this work, a method is proposed to impair these disadvantages. By considering local smoothness of the sky region, pixels in the bright regions (such as light source, white object, etc.) of the haze image are first excluded from being mistaken as reference pixels, so that the estimation of subsequent parameters is more accurate. Meanwhile, the pixels of the most occurrences are selected to replace the ones in the neighborhood centered on the edge pixels, and the operation is carried out pixel by pixel along the edge contour in replace of the minimum filter. This makes the estimation of transmission near the edge in better agreement with the actual situation, and can effectively avoid the “white haze” which is an artifact caused by the minimum filtering in the areas with the depth of field changing dramatically. Compared with the previous methods, the proposed algorithm in this paper can restore images more clearly, with more image edge details retained, and effectively improve visual effect of the scene in haze weather.