Single-image dehazing based on dark channel prior and fast weighted guided filtering

Abstract

Haze scatters light transmitted in the air and reduces image quality, which greatly decreases the interpretability and intelligibility of an image. To solve these problems, we propose an improved real-time image dehazing algorithm based on dark channel prior and fast weighted guided filtering. First, the image is divided into dark areas and bright areas by the K-means clustering algorithm, and the atmospheric light value is calculated according to the proportion of the bright area in the whole image. Second, the fast weighted guided filtering algorithm is employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Finally, the gamma correction and automatic contrast enhancement algorithms are used to adjust the brightness and contrast of the dehazing image. Experimental results demonstrate that the proposed method can effectively remove the halo artifacts, improve the color deviation, and retain more details in the images.