Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex

doi:10.1049/iet-ipr.2016.0972

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 图像与视频分析团队

	Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex
	Liu, Haoting 1; Lu, Hanqing1 ; Zhang, Yu 2
刊名	IET IMAGE PROCESSING
	2017-09-01
卷号	11 期号:9 页码:786-795
关键词	Image Enhancement Video Surveillance Image Restoration Wavelet Transforms Neural Nets Backpropagation Outdoor Long-range Surveillance Blind Iq-learning Multiscale Retinex Visible Light Camera-based Image Enhancement Method Blind Image Quality Learning Multiscale Retinex Image Brightness Degree Image Region Contrast Degree Image Edge Blur Degree Image Colour Quality Degree Image Noise Degree Wavelet Transform Multiscale Retinex Wt_msr Multiple Optimal Control Parameter Mocp Backpropagation Neural Network Bpnn
DOI	10.1049/iet-ipr.2016.0972
文献子类	Article
英文摘要	The visible light camera-based long-range surveillance always suffers from the complex atmosphere. When applying some traditional image enhancement methods, the computational effects behave limited because of their poor environment adaptability. To conquer that problem, a blind image quality (IQ) learning-based multiscale Retinex, i.e. the IQ-learning multiscale Retinex, is proposed. First, a series of typical degenerated images are collected. Second, several blind IQ evaluation metrics are computed for the dataset above. They are the image brightness degree, the image region contrast degree, the image edge blur degree, the image colour quality degree, and the image noise degree. Third, a wavelet transform multi-scale Retinex (WT_MSR) is used to carry out the basic image enhancement. A kind of optimal enhancement is implemented by the subjective evaluation and tuning of multiple optimal control parameters (MOCPs) of WT_MSR for these degenerated dataset. Fourth, the back propagation neural network (BPNN) is used to build a connection between the IQ metrics and the MOCPs. Finally, when a new image is captured, this system will compute its IQ metrics and estimate the MOCPs for the WT_MSR by BPNN; then a kind of optimal enhancement can be realised. Many outdoor applications have shown the effectiveness of proposed method.
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000410158000014
资助机构	National Natural Science Foundation of China(61501016)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/20724]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Astronaut Res & Training Ctr China, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Haoting,Lu, Hanqing,Zhang, Yu. Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex[J]. IET IMAGE PROCESSING,2017,11(9):786-795.
APA	Liu, Haoting,Lu, Hanqing,&Zhang, Yu.(2017).Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex.IET IMAGE PROCESSING,11(9),786-795.
MLA	Liu, Haoting,et al."Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex".IET IMAGE PROCESSING 11.9(2017):786-795.