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 Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator
Tác giả hoặc Nhóm tác giả: Dat Ngo, Seungmin Lee, Ui-Jean Kang, Tri Minh Ngo, Gi-Dong Lee and Bongsoon Kang
Nơi đăng: Sensors; Số: 22;Từ->đến trang: 1957;Năm: 2022
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Haze is the most frequently encountered weather condition on the road, and it accounts fora 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 densityestimate. 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.
ABSTRACT
Haze is the most frequently encountered weather condition on the road, and it accounts fora 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 densityestimate. 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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