Key Technology Research and Development Program of Shaanxi Province (No.2015GY052);Fund of Xi'an Science and Technology Bureau (No.CXY1512 (9), No.CXY1437-9)
WEN Li-min, JU Yong-feng, YAN Mao-de. Inspection of Fog Density for Traffic Image Based on Distribution Characteristics of Natural Statistics[J]. Acta Electronica Sinica, 2017, 45(8): 1888-1895.
DOI:
WEN Li-min, JU Yong-feng, YAN Mao-de. Inspection of Fog Density for Traffic Image Based on Distribution Characteristics of Natural Statistics[J]. Acta Electronica Sinica, 2017, 45(8): 1888-1895. DOI: 10.3969/j.issn.0372-2112.2017.08.012.
Inspection of Fog Density for Traffic Image Based on Distribution Characteristics of Natural Statistics
Concerning low efficiency detection for foggy density in traffic image
a novel algorithm was proposed to check foggy density based on distribution characteristics of natural statistics.Firstly
foggy images were partitioned as PP pixel patches by method of the maximum overlap count.Secondly
featured function vector for local contrast and entropy was created and maximum likelihood estimation between tested image and two standard image corpuses were respectively computed.Finally
Mahalanobis-like Distances(MD) between foggy image and corpus of standard foggy image or fog-free image were achieved
and the ratio of two values could be used to measure the foggy density.Simulation shows that the value D can respond the varied tendency to density for same scene with difference density or different scene with different density.Correlation coefficient up to 0.97 between this algorithm and mean opinion scores (MOS) method indicate high linear about them and the coefficient is larger than 0.56 between mean subtracted contrast normalized (MSCN) and MOS.Comparison to PM2.5 shows that this algorithm can be used to evaluate the level for fog density.
Image Dehazing Based on Gradient Guided Polarization Degree Estimation
Detection of Driving State Under Different Curve Road based on Entropy and Functional Connectivity of EEG
DRHA-UIE: An Underwater Image Enhancement Method Based on Dual Residual Hybrid Attention Block
Image Harmonization Guided by Semantic Information
MalMKNet: A Multi-Scale Convolutional Neural Network Used for Malware Classification
Related Author
XU Wan-chun
ZHANG Yan
ZHANG Jing-hua
LING Feng
LI Shun
CHANG Wen-wen
YAN Guang-hui
YANG Zhi-fei
Related Institution
National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology
Academy of Military Science
College of Electronic Science and Technology, National University of Defense Technology
School of Electronic and Information Engineering, Lanzhou Jiaotong University
College of Computer Science and Technology, Jilin University