CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex
Liu, Haoting1; Lu, Hanqing1; Zhang, Yu2
Source PublicationIET IMAGE PROCESSING
2017-09-01
Volume11Issue:9Pages:786-795
SubtypeArticle
AbstractThe 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.
KeywordImage 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
WOS HeadingsScience & Technology ; Technology
DOI10.1049/iet-ipr.2016.0972
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61501016)
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000410158000014
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20724
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Astronaut Res & Training Ctr China, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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