Detail Preserved Single Image Dehazing Algorithm Based on Airlight Refinement

Abstract

Single-image haze removal is important for many practical applications (e.g., surveillance). However, dehazed results of existing algorithms tend to be oversmoothed with missing fine image details. This drawback is caused by two factors: inaccurate airlight estimations and disregarding multiple scattering. In this paper, we propose a detail-preserving image dehazing algorithm based on two key priors, namely, the depth-edge aware prior and the airlight impact regularity prior. The proposed algorithm makes contributions in both the haze removal step and the postprocessing step. First, based on the depth-edge aware prior, an airlight refinement algorithm is proposed. The gradient strength of the minimum channel is employed to calculate punishment weights to smooth the dark channel. Second, based on the airlight impact regularity prior, an adaptive sharpening model that considers the refined airlight to determine the sharpening strength value is established to enhance levels of detail. Experimental results demonstrate that the proposed algorithm cannot only effectively remove haze but can also enhance levels of detail to thus outperform the state of the art on a wide variety of images.