Adaptive transmission compensation via human visual system for efficient single image dehazing

Dark channel prior has been used widely in single image haze removal because of its simple implementation and satisfactory performance. However, it often results in halo artifacts, noise amplification, over-darking, and/or over-saturation for some images containing heavy fog or large sky patches where dark channel prior is not established. To resolve this issue, this paper proposes an efficient single dehazing algorithm via adaptive transmission compensation based on human visual system (HVS). The key contributions of this paper are made as follows: firstly, two boundary constraints on transmission are deduced to preserve the intensity of the defogged image and suppress halo artifacts or noise via the minimum intensity constraint and the just-noticeable distortion model, respectively. Secondly, an improved HVS segmentation algorithm is employed to detect the saturation areas in the input image. Finally, an adaptive transmission compensation strategy is presented to remove the haze and simultaneously suppress the halo artifacts or noise in the saturation areas. Experimental results indicate that this proposed method can efficiently improve the visibility of the foggy images in the challenging condition.