@article{ref1,
title="Adapting a dehazing system to haze conditions by piece-wisely linearizing a depth estimator",
journal="Sensors (Basel)",
year="2022",
author="Ngo, Dat and Lee, Seungmin and Kang, Ui-Jean and Ngo, Tri Minh and Lee, Gi-Dong and Kang, Bongsoon",
volume="22",
number="5",
pages="e1957-e1957",
abstract="Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22051957",
url="http://dx.doi.org/10.3390/s22051957"
}