Built upon an image formation model for a single hazy image, existing image dehazing methods typically restore hazed pixels by estimating the unknown transmission map and global ambient light via exploiting image priors. They often produce visually unpleasing results when hazy images are with unwanted color shifts due to inaccurate estimation about the actual ambient light of hazy images with color shifts. To address the problem, we propose a novel color shifting-aware image dehazing model that explicitly disentangles the inference of the image formation model. Specifically, our model attempts to calibrate color fading and shifting first, and then restores the hazed pixels via the scene depth based gamma correction using the color-corrected image as the guidance. Extensive experiments show that the proposed dehazing model significantly outperforms existing dehazing methods and achieves superior dehazing results on challenging cases with unwanted color casts.
2019 IEEE International Symposium on Multimedia (ISM), University of California, Irvine