Variational Formulation of Dark Channel Prior for Single Image Dehazing

In this paper, we analyze the single image dehazing problem and propose a new variational method to solve it based on the dark channel prior. In the analysis section, we determine the influence that error in estimation of parameters of the haze degradation model has on the reconstructed image and give conclusions that can be used in designing a dehazing method. After that, we use those conclusions to bias our variational method as well as create a smooth variant of the dark channel prior, so it can be directly used in variational methods as well as potentially deep learning methods. We compare the proposed method quantitatively on a synthetic hazy image dataset as well as qualitatively on real-life hazy images.