Improved color attenuation prior based image de-fogging technique

Abstract

Single image de-fogging has been a confronting problem due to its ill-posed nature. In this paper, a de-fogging technique based upon color attenuation prior (CAP) has been proposed. CAP uses linear model and learning the parameters of this model with a supervised learning method for estimating the scene depth of a foggy image. This depth map is utilized for transmission map estimation. Transmission (t) is one of the important parameter of physical model based de-fogging techniques which describes the portion of the light coming from the scene point that is not scattered and reaches the camera. It is a map called transmission or transparency of the fog 0 < t(x) < 1, t(x) = 0 means completely foggy, t(x) = 1 means fog-free. The more accurately the transmission or depth is estimated, the better the defogging performance will be. In the proposed work, to quickly and accurately estimate the transmission map, a sub-sampling based local minimum operation and fast gradient domain guided image filtering (GDGF) is applied on CAP based initial depth map. The edge attentive restraints of GDGF make edges to be conserved better in the de-fogged images. The de-fogged images obtained by CAP technique suffer from dullness and higher illumination variations due to consideration of fog image degradation model in homogeneous environment and a constant value of atmospheric light. Such variations are removed in the proposed work by using Lambert’s law of illumination reflection, which helps to compensate non uniform illumination, causes simultaneous dynamic range modification, color consistency, and lightness rendition without producing the artifacts in a de-fogged image. To improve the processing speed, image sub-sampling mechanism is used in various steps of image de-fogging. The sub-sampling is used in such a way that the quality of the output at any step is not compromised as demonstrated through various quality parameters. Experimental results show that the proposed approach outperforms state-of-the-art fog removal techniques in terms of efficiency and the de-fogging effect.