Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator.
Sensors (Basel)
; 22(5)2022 Mar 02.
Article
en En
| MEDLINE
| ID: mdl-35271107
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.
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1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
Idioma:
En
Revista:
Sensors (Basel)
Año:
2022
Tipo del documento:
Article