基于余弦值的红外光强与偏振图像特征差异度的度量方法
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摘要
现有差异特征驱动的红外光强与偏振图像融合方法中,特征的选择上数量多,存在信息冗余,通过提取图像特征再求差来获取差异特征不能充分表征图像间的差异。本文提出一种基于余弦值的红外光强与偏振图像特征差异度的度量方法。通过对源图像提取特征图,将亮度、纹理、边缘三类特征进行分离,避免了图像特征选择的困难;定义了特征差异度,从特征图提取得到特征向量,再计算其差异度,对图像的差异进行了量化;最后,通过实验验证了所提方法的合理性和有效性。
In the intensity and polarization image fusion method driven by the difference features, the number of image features is large, and there is information fusion redundancy among the features. In this paper, a method of measuring the feature difference between infrared intensity and the polarization image based on cosine value is presented. By extracting the feature image from the source image, separating luminance, texture, and edge features, the difficulty of feature selection is avoided. Meanwhile, the feature difference degree is defined and calculated based on the feature vectors extracted from the feature map. The difference between the images is quantified in this way. Finally, the rationality and effectiveness of the proposed method are verified through experiments.
In the intensity and polarization image fusion method driven by the difference features, the number of image features is large, and there is information fusion redundancy among the features. In this paper, a method of measuring the feature difference between infrared intensity and the polarization image based on cosine value is presented. By extracting the feature image from the source image, separating luminance, texture, and edge features, the difficulty of feature selection is avoided. Meanwhile, the feature difference degree is defined and calculated based on the feature vectors extracted from the feature map. The difference between the images is quantified in this way. Finally, the rationality and effectiveness of the proposed method are verified through experiments.
引文
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[13]YANG W,YANG H Y,GAO Q.Insulator Species Recognition Based on Difference Features of the Color Image[J].Applied Mechanics&Materials,2014,631-632:441-445.
[14]周萧,杨风暴,蔺素珍,等.双色中波红外图像差异统计特征的选择与提取[J].应用光学,2012,33(4):721-726.ZHOU Xiao,YANG Fengbao,LIN Suzhen,et al.Selection and extraction of difference statistical features based on dual color MWIRimages[J].Journal of Applied Optics,2012,33(4):721-726.
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[20]Davarzani R,Mozaffari S,Yaghmaie K.Scale-and rotation-invariant texture description with improved local binary pattern features[J].Signal Processing,2015,111:274-293.
[21]Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on feature distributions[J].Pattern Recognition,1996,29(1):51-59.
[2]WANG X,LIANG J,LONG H,et al.Experimental study on long wave infrared polarization imaging of typical background and objectives[J].Infrared&Laser Engineering,2016,45(7):32-38.
[3]夏寅辉,骆守俊,白廷柱,等.红外偏振在目标探测识别中的研究进展[J].激光与红外,2016,46(8):909-915.XIA Yinhui,LUO Shoujun,BAI Tingzhu,et al.Research progress of infrared polarization in target detection and recognition[J].Laser&Infrared,2016,46(8):909-915.
[4]LI Xiaoming,HUANG QinChao.Target detection for infrared polarization image in the background of desert[J].International Conference on Communication Software and Networks(ICCSN),2017(9):1147-1151.
[5]NIU T,YANG F B,WANG X X,et al.Establishment of set-valued mapping between difference characteristics and fusion algorithms[J].Infrared and Laser Engineering,2015,44(3):1073-1079.
[6]LIANG J,ZHANG W,REN L,et al.Polarimetric dehazing method for visibility improvement based on visible and infrared image fusion[J].Applied Optics,2016,55(29):8221-8226.
[7]LI H,YU Z,MAO C.Fractional differential and variational method for image fusion and super-resolution[J].Neurocomputing,2015,171(C):138-148.
[8]Bavirisetti D P,Dhuli R.Two-scale image fusion of visible and infrared images using saliency detection[J].Infrared Physics&Technology,2016,76:52-64.
[9]苗启广,叶传奇,汤磊.多传感器图像融合技术及应用[M].西安:西安电子科技大学出版社,2014.MIAO Qiguang,YE Chuanqi,TANG Lei.Technology and Application of Multi-Sensor Image Fusion[M].Xi'an:Xi'an University of Electronic Science and Technology Press,2014.
[10]周萧,杨风暴,安富,等.双色中波红外图像差异纹理特征分析方法研究[J].红外技术,2012,34(6):340-345.ZHOU Xiao,YANG Fengbao,AN Fu,et al.The Analysis Method of Differences in Image Texture Features Based on Dual-color MWIRImages[J].Infrared Technology,2012,34(6):340-345.
[11]牛涛,杨风暴,卫红,等.红外偏振和光强图像差异特征分类树的构建[J].红外技术,2015,37(6):457-461.NIU Tao,YANG Fengbao,WEI Hong,et al.Classification Tree of Difference Features between Infrared Polarization and Infrared Intensity Images[J].Infrared Technology,2015,37(6):457-461.
[12]杨红叶.基于图像差异性特征描述的目标识别方法及其应用[D].北京:华北电力大学,2015.YANG Hongye.Target Recognition Method Based on Difference Features Description of Image and Its Application[D].Beijing:North China Electric Power University,2015.
[13]YANG W,YANG H Y,GAO Q.Insulator Species Recognition Based on Difference Features of the Color Image[J].Applied Mechanics&Materials,2014,631-632:441-445.
[14]周萧,杨风暴,蔺素珍,等.双色中波红外图像差异统计特征的选择与提取[J].应用光学,2012,33(4):721-726.ZHOU Xiao,YANG Fengbao,LIN Suzhen,et al.Selection and extraction of difference statistical features based on dual color MWIRimages[J].Journal of Applied Optics,2012,33(4):721-726.
[15]陈飞.扫描半色调图像去网纹算法研究[D].长沙:湖南大学,2015.CHEN Fei.Researches on Scanned Halftone Image Descreening Algorithm[D].Changsha:Hunan University,2015.
[16]XU L,YAN Q,XIA Y,et al.Structure extraction from texture via relative total variation[J].Acm Transactions on Graphics,2012,31(6):139.
[17]Ojala T,Pietik,Inen M,et al.Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[M].Computer Vision-ECCV 2000.Springer Berlin Heidelberg,2000:404-420.
[18]Brenner C.Global Localization of Vehicles Using Local Pole Patterns[C]//Dagm Symposium on Pattern Recognition.Springer-Verlag New York,2009:61-70.
[19]ZHAO Y,JIA W,HU R X,et al.Completed robust local binary pattern for texture classification[J].Neurocomputing,2013,106(6):68-76.
[20]Davarzani R,Mozaffari S,Yaghmaie K.Scale-and rotation-invariant texture description with improved local binary pattern features[J].Signal Processing,2015,111:274-293.
[21]Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on feature distributions[J].Pattern Recognition,1996,29(1):51-59.