Single image dehazing with bright object handling
- Authors
- Riaz, Irfan; Fan, Xue; Shin, Hyunchul
- Issue Date
- Dec-2016
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Keywords
- ADAPTIVE DARK CHANNEL; HAZE REMOVAL; ENHANCEMENT; FRAMEWORK; WEATHER; VISION
- Citation
- IET COMPUTER VISION, v.10, no.8, pp.817 - 827
- Indexed
- SCIE
SCOPUS
- Journal Title
- IET COMPUTER VISION
- Volume
- 10
- Number
- 8
- Start Page
- 817
- End Page
- 827
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/12186
- DOI
- 10.1049/iet-cvi.2015.0451
- ISSN
- 1751-9632
- Abstract
- This study addresses the shortcomings of the dark channel prior (DCP). The authors propose a new and efficient method for transmission estimation with bright-object handling capability. Based on the intensity value of a bright surface, they categorise DCP failures into two types: (i) obvious failure: occurs on surfaces that are brighter than ambient light. They show that, for these surfaces, altering the transmission value proportional to the brightness is better than the thresholding strategy; (ii) non-obvious failure: occurs on surfaces that are brighter than the neighbourhood average haziness value. Based on the observation that the transmission of a surface is loosely connected to its neighbours, the local average haziness value is used to recompute the transmission of such surfaces. This twofold strategy produces a better estimate of block and pixel-level haze thickness than DCP. To reduce haloes, a reliability map of block-level haze is generated. Then, via reliability-guided fusion of block-and pixel-level haze values, a high-quality refined transmission is obtained. Experimental results show that the authors' method competes well with state-of-the-art methods in typical benchmark images while outperforming these methods in more challenging scenarios. The authors' proposed reliability-guided fusion technique is about 60 times faster than other well-known DCP-based approaches.
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