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Low Light Image Enhancement Based on Luminance map and Haze Removal Model

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成果类型:
会议论文
作者:
Xie Wei*谢伟);Long Xueling;Tu Zhigang;Yu Jin;Xu Ke
通讯作者:
Xie Wei
作者机构:
[Xie Wei; Xu Ke; Yu Jin; Long Xueling] Cent China Normal Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
[Tu Zhigang] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore.
通讯机构:
[Xie Wei] C
Cent China Normal Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
语种:
英文
关键词:
low light images;luminance map;haze removal model;combined denoising
期刊:
2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2
ISSN:
2165-1701
年:
2017
页码:
143-146
会议名称:
2017 10th International Symposium on Computational Intelligence and Design (ISCID)
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61501198, 41671377, 41501463]; National Science Foundation of Hubei Province [2014CFB461]; Wuhan Youth Science and Technology Cheuguang program [2014072704011248]
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
In this paper, a novel and effective algorithm is proposed for noise reduction and contrast enhancement in low light images based on luminance map and haze removal model. The proposed method is divided into two steps: i) A combined denoising method using the improved guided filtering based on gradient information and median filtering is proposed to obtain the initial denoised image. ii)Considering that an inverted low light image presents quite similar to a haze image, the haze removal model is used to enhance the denoised low light image. The luminance component L is extracted to obtain the t...

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