A General-Purpose Dehazing Algorithm based on Local Contrast Enhancement Approaches

TL;DR

This paper introduces a fast, versatile dehazing algorithm based on local contrast enhancement that does not require training, making it suitable for real-time applications and compatible with various vision tasks.

Contribution

The proposed method is a training-free, fast dehazing algorithm that can be integrated as a pre- or post-processing step, applicable to CPU and GPU, and demonstrates promising results.

Findings

Effective dehazing without training or optimization

Compatible with real-time applications on CPU and GPU

Shows competitive results compared to state-of-the-art methods

Abstract

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.