Single image haze removal using content-adaptive dark channel and post enhancement

As a challenging problem, image haze removal plays an important role in computer vision applications. The dark channel prior has been widely studied for haze removal since it is simple and effective; however, it still suffers from over-saturation, artefacts and dark-look. To resolve these problems, this study proposes a method of single image haze removal using content-adaptive dark channel and post enhancement. The main contributions of this work are as follows: first, an associative filter, which can transfer the structures of a reference image and the grey levels of a coarse image to the filtering output, is employed to compute the dark channel efficiently and effectively. Secondly, the dark channel confidence is utilised to restrict the dark channel based on the content of the image. Finally, a post enhancement method is devised to map the luminance of the restored haze-free image with the preservation of local contrast. Experimental results demonstrate that the proposed method significantly improves the visibility of the hazy image.