Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization

It is necessary to perform fog removal from an image based on the estimation of depth to increase the visibility of a scene. In this paper, we propose a new algorithm to eradicate fog from images in which fog is defined as a state or cause of perplexity or confusion with respect to the image. It runs at high speed and simultaneously minimizes the halo-artifact with a new median operator in dark channel prior. The proposed method is based on Guided Filter for transmission-map refinement and Contrast Limited Adaptive Histogram Equalization (CLAHE) for visibility improvement. It preserves small details while remaining robust against density of fog, and recovers scene contrast simultaneously. Guided filter improved the transmission map acquired from Median dark channel prior (MDCP), which is an improvement of the Dark Channel Prior DCP by the use of median operation. All of the parameters used in our method are data driven. The quality of algorithm has been validated on several types of fog-degraded images where considerable variation in contrast and illumination exists. Moreover, its performance is compared with the other state-of-the-art methods. The experimental results indicate that the proposed method effectively restores the color and contrast of scene as well as produces satisfactory information in homogeneous fog. It outperforms the existing fog removal methods for run time computational time and other evaluation metrics for rating of visibility enhancement. The proposed method conserves small details part of the image when outstanding vigorous against concentration of fog, and recuperate scene contrast instantaneously. It controls at a high speed than the existing approaches and can diminish the halo effect.