基于自然统计特征分布的交通图像雾浓度检测

doi:10.3969/j.issn.0372-2112.2017.08.012

电子学报 ›› 2017, Vol. 45 ›› Issue (8): 1888-1895.DOI: 10.3969/j.issn.0372-2112.2017.08.012

基于自然统计特征分布的交通图像雾浓度检测

温立民, 巨永锋, 闫茂德

长安大学电控学院, 陕西西安 710064

收稿日期:2016-12-06 修回日期:2017-03-29 出版日期:2017-08-25
- 通讯作者:
- 温立民
- 作者简介:
- 巨永锋,男,1962年生于陕西周至,长安大学电子与控制工程学院教授,博士生导师,硕士生导师,主要研究方向交通智能测控技术及应用、图像处理与机器视觉等.
- 基金资助:
- 陕西省科技攻关项目 (No.2015GY052）; 西安市科技局基金 (No.CXY1512 (9），No.CXY1437-9）

Inspection of Fog Density for Traffic Image Based on Distribution Characteristics of Natural Statistics

WEN Li-min, JU Yong-feng, YAN Mao-de

School of Electronic & Control Engineering, Chang'an University, Xi'an, 710064, China

Received:2016-12-06 Revised:2017-03-29 Online:2017-08-25 Published:2017-08-25

摘要/Abstract

摘要： 针对交通场景图像去雾中缺乏有效浓度检测的不足，提出基于自然统计特征分布的雾浓度检测算法.首先，将待检图像分为P×P大小的子块；其次，建立图像局部对比度及熵的自然统计特性向量，求解待检图像和标准图像子集间最佳期望及协方差的最大似然估计；最后，分别计算待检图像与有雾和无雾标准子集间的马氏距离，以二者的比值D作为场景雾浓度的度量.通过同一场景不同浓度等级和不同场景不同雾浓度等级的图像测试，表明算法D值能正确反应雾浓度的变化趋势；通过与标准主观评价法（MOS）比较，表明二者呈现近似的线性，相关系数可达0.97，远高于归一化亮度系数（MSCN）的0.56；通过与PM2.5比较，表明算法能准确的评定雾浓度等级.

关键词: 雾, 图像处理, 浓度, 对比度, 熵

Abstract: 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 P×P 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.

Key words: fog, image processing, density, contrast, entropy

中图分类号:

TP391.4

温立民, 巨永锋, 闫茂德. 基于自然统计特征分布的交通图像雾浓度检测[J]. 电子学报, 2017, 45(8): 1888-1895.

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.

参考文献

[1] 禹晶,徐东彬,廖庆敏.图像去雾技术研究进展[J].中国图象图形学报,2011,16(9):1561-1576. Yu J,Xu D B,Liao Q M.Image defogging:a sruvey[J].Journal of Image and Graphics,2011,16(9):1561-1576.(in Chinese)
[2] MITTAL.A,MOORTHY A K,BOVIK A G.No-reference image quality assessment in the spatial domain[J].IEEE Transactions on Image Processing,2012,21(12):4695-4708.
[3] POMERLEAU D.Visibility estimation from a moving vehicle using the RALPH vision system[J].IEEE Intell.Transp Syst,1997:906-911.
[4] SRINIVASA G.Narasimhan,Shree K Nayar.Contrast restoration of weather degraded images[J].IEEE Trans.Pattern Anal.Mach.Intell,2003,25(6):713-724.
[5] SCHECHNER Y,Srinivasa Narasimhan,Shree K Nayar.Instant dehazing of images using polarization[A].IEEE Conf Computer Vis Pattern Recognit.,2001,1(10):I-325-I-332.
[6] ANCUTI C O,ANCUTI C.Single image dehazing by multi-scale fusion[J].IEEE Trans on Image Processing,2013,22(8):3271-3282.
[7] 陈勇,帅峰,樊强.基于自然统计特性分布的无参考图像质量评价[J].电子与信息学报,2016,38(7):1645-1653. Chen Y,Shuai F,Fan Q.A no-reference image quality assessment based on distribution characteristics of natural statistics[J].Journal of Electronics & Information Technology,2016,38(7):1645-1653.(in Chinese)
[8] SHEIKH H R,WANG Z,CORMACKL,et al.LIVE image quality assessment database,Version 2[DB/OL].http://live.ece.utexas.edu/research/quality,2014.
[9] GROEN I I A,GHEBREAB S,PRINS H,LAMME V A F,SCHOLTE H S.From image statistics to scene gist:Evoked neural activity reveals transition from low-level natural image structure to scene category[J].J Neurosci,2013,33(48):8814-8824.
[10] 付晓薇,代芸,陈黎,等.基于局部熵的量子衍生医学超声图像去斑[J].电子与信息学报,2015,37(3):560-566. FU X W,Dai Y,Chen L,et al.Quantum-inspired despeckling of medical ultrasound images based on local entropy[J].Journal of Electronics & Information Technology,2015,37(3):560-566.(in Chinese)
[11] HASLER D,SUESSTRUNK S E.Measuring colorfulness in natural images[J].Proc.SPIE,2003,5007:87-95.(in Chinese)
[12] CHOI L K,YOU J,BOVIK A C.Referenceless prediction of perceptual fog density and perceptual image defogging[J].IEEE Transactions on Image Processing.2015,24(11):3888-3901.
[13] 刘培,王建英,尹忠科.图像光照补偿方法研究[J].系统工程与电子技术,2008,30(7):1343-1346. Liu P,Wang J Y,Yin Z K.Study on Illumination Compensation Method for Images[J].System Engineering and Electrionics,2008,30(6):1343-1346.(in Chinese)
[14] 徐晓昭,蔡轶珩,刘晓民.改进灰度世界颜色校正算法[J].光子学报,2010,39(3):559-564. Xu X Z,Cai Y H,Liu X M.Improved gray world color correction algorithms[J].Acta Photonica Sinica.2010,39(3).559-564.(in Chinese)
[15] International Telecommunications Union-Radiocommunications Sector (ITU-R).Methodology for the Subjective Assessment of the Quality of Television Pictures[S].Document ITU BT-500-11,2002.
[16] Zhu Z M,Qu X H,Liang H Y.Effect of color Illumination on color contrast in color vision application[J].Proc,SPIE,2010,7855:785510-7855108.
[17] 王志明.无参考图像质量评价综述[J].自动化学报,2015,41(6):1062-1079. Wand Z M.Review of no-reference image quality assessments[J].ACTA Automatica Sinica,2015,41(6):1062-1079.(in Chinese)

基于自然统计特征分布的交通图像雾浓度检测

Inspection of Fog Density for Traffic Image Based on Distribution Characteristics of Natural Statistics

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