Dense Haze Removal by Nonlinear Transformation

Abstract

Images captured in hazy or foggy weather conditions, suffer from various problems, such as limited visibility, low contrast, color distortions. These images are used in many computer vision applications, such as video surveillance, transportation, remote sensing. The elimination of the haze effect from these images is essential to ensure the perfect working of these applications. The degradation of a captured image is expressed by the physical model of hazy image formation. The physical model requires the estimation of transmission to restore a haze-free image, which is one of the most important parameters of single image dehazing (SID). Due to the ill-posed nature of SID, lots of priors/assumptions have been used. However, traditional methods fail when these priors do not hold, especially for varying haze concentrations, which lead to many issues such as incomplete haze removal or over enhancement in long-range regions. In this paper, a single image dehazing method based on a superpixel and nonlinear transformation is proposed. The proposed method transforms the minimum filtering on superpixels of a hazy image into the minimum filtering on superpixels of a haze-free image using nonlinear transformation. The nonlinear transformation prevents over enhancement in the long-range regions, while the superpixels reduce the halo artifacts in the dehazed image. The experimental results on challenging real hazy images, dense-hazy images, and synthetic images have proved that a combination of nonlinear transformation and superpixels provide the strength to the proposed method. The obtained dehazed results are evaluated qualitatively and quantitatively and it is found that the proposed method has tremendous performance as compared to state-of-the-art dehazing approaches.