신현철
2018-05-14T06:24:40Z
2018-05-14T06:24:40Z
2016-12
IET COMPUTER VISION, v. 10, No. 8, Page. 817-827
1751-9632
1751-9640
http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0451
https://repository.hanyang.ac.kr/handle/20.500.11754/71364
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.
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MOE) (NRF-2013R1A1A2004421). Moreover, the author Irfan Riaz is sponsored by the Higher Education Commission (HEC) of the Government of Pakistan.
en_US
INST ENGINEERING TECHNOLOGY-IET
ADAPTIVE DARK CHANNEL
HAZE REMOVAL
ENHANCEMENT
FRAMEWORK
WEATHER
VISION
Single image dehazing with bright object handling
Article
8
10
10.1049/iet-cvi.2015.0451
817-827
IET COMPUTER VISION
Riaz, Irfan
Fan, Xue
Shin, Hyunchul
2016000250
E
COLLEGE OF ENGINEERING SCIENCES[E]
DIVISION OF ELECTRICAL ENGINEERING
shin