Weighted Dark Channel Dehazing
This work addresses image dehazing for computer vision applications, representing an incremental improvement over existing dark channel methods.
The paper tackles the problem of defects in dark channel dehazing methods caused by the local constant assumption, by splitting the dark channel concept and controlling the assumption with a novel weight map, resulting in significant quality improvement and competitive speed.
In dark channel based methods, local constant assumption is widely used to make the algorithms invertible. It inevitably introduces defects since the assumption can not perfectly avoid depth discontinuities and meanwhile cover enough pixels. Unfortunately, because of the limitation of the prior, which only confirms the existence of dark things but does not specify their locations or likelihood, no fidelity measurement is available in refinement thus the defects 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 pixels and local constant assumption, and then, control the problematic assumption based on a novel weight map. With such effort, our methods show significant improvement on quality and have competitive speed. In the last, we show that the method is highly robust to initial transmission estimates and can be ever-improved by providing better dark pixel locations.