  Dehazing is in the image processing and computer vision communities, the task
of enhancing the image taken in foggy conditions. To better understand this
type of algorithm, we present in this document a dehazing method which is
suitable for several local contrast adjustment algorithms. We base it on two
filters. The first filter is built with a step of normalization with some other
statistical tricks while the last represents the local contrast improvement
algorithm. Thus, it can work on both CPU and GPU for real-time applications. We
hope that our approach will open the door to new ideas in the community. Other
advantages of our method are first that it does not need to be trained, then it
does not need additional optimization processing. Furthermore, it can be used
as a pre-treatment or post-processing step in many vision tasks. In addition,
it does not need to convert the problem into a physical interpretation, and
finally that it is very fast. This family of defogging algorithms is fairly
simple, but it shows promising results compared to state-of-the-art algorithms
based not only on a visual assessment but also on objective criteria.
