Boosting dark channel dehazing via weighted local constant assumption

Abstract

In dark channel based methods, local constant assumption is widely used to make the algorithms invertible. It inevitably introduces outliers since the assumption can not perfectly avoid depth discontinuities and meanwhile cover enough pixels. Unfortunately, because of the limitation of the dark channel prior, which only confirms the existence of dark things but does not specify their locations or likelihoods, no fidelity measurement is available in refinement. Therefore, the outliers are either under-corrected or over-corrected. In this paper, we go deeper than the dark channel theory to overcome this problem. We split the concept of dark channel into dark pixel and local constant assumption, and then, propose a novel weight map. With such effort, our method shows improvements on quality, robustness and speed. The theory is even simpler and more intuitive than the original, leading to the refreshingly concise algorithm. In the last, we show that the results can be ever-improved by scribbles, which indicate better dark pixel locations.