Improving DCP Haze Removal Scheme by Parameter Setting and Adaptive Gamma Correction

Abstract

Recently, single-image haze removal based on the dark channel prior (DCP), originally proposed by He et. al., has attracted much attention in the image restoration community. This dehazing algorithm, called the DCP scheme here, is well-known to have four main problems in its dehazed images: artifacts, hue distortion, color over-saturation, and halos. In this paper, an improved DCP (IDCP) is proposed to deal with the four aforementioned problems, by setting the model parameters, i.e. scaling factors and window size and smoothing factor of a guided image filter in the DCP scheme. Note that a dehazed image is generally dim and low in contrast. An adaptive gamma correction (AGC) is introduced for dehazed image enhancement. The proposed IDCP and AGC are used to create the IDCP/AGC scheme, in which the IDCP scheme performs haze removal and the AGC enhances the dehazed image. The IDCP/AGC scheme was justified through extensive experiments and compared with the DCP scheme, an optimization-based scheme, and two learning-based schemes on two datasets. The results indicated that the proposed scheme is subjectively and objectively superior to the comparison schemes.