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Outdoor images taken in foggy weather are not suitable for automation due to low contrast. It is a challenging task to remove fog from images specially when the image contains large sky region. The authors propose dark channel-based single image defogging technique to estimate atmospheric light which represents the amount of luminance in a scene in the absence of fog. This atmospheric light is used to reconstruct fog-free image with a transmission map. Transmission map represents the effect of fog with respect to depth in image. In this study, they propose four transmission maps to reconstruct the images with different colour contrast. Proposed method adaptively selects a transmission map depending upon the fog density to reconstruct image with optimal colour contrast. The transmission map is refined by applying Laplacian filter followed by the guided filter. Previously, dark channel prior based methods were considered to be less effective for images with large sky region, but the proposed method reconstructs better result consistently for such images, independent of the density of the fog. Experimental results show that images reconstructed by proposed method are qualitatively better than the previously proposed methods.

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