Contrast enhancement based single image dehazing VIA TV-l1 minimization

In this paper, we propose a general algorithm to removing haze from single images using total variation minimization. Our approach stems from two simple yet fundamental observations about haze-free images and the haze itself. First, clear-day images usually have stronger contrast than images plagued by bad weather; and second, the variations in natural atmospheric veil, which highly depends on the depth of objects, always tend to be smooth. Integrating these two criteria together leads to a new effective dehazing model, which encourages the gradient ℓ₁ sparsity of atmospheric veil and implicitly maximizes the global contrast of haze-free image in the meanwhile. We also show that the proposed dehazing model can be efficiently solved using the TV-ℓ₁ minimization. Compared to alternative state-of-the-art methods, our approach is physically plausible and works well for all types of hazy situations. Comparative study and quantitative evaluation on both synthetic and natural images validate the superior performance and the generality of our approach.