多传感器图像融合理论及其应用研究
|
摘要
多传感器图像融合是以图像为研究对象的信息融合,它把对同一目标或场景的用不同传感器获得的不同图像,或用同种传感器以不同成像方式或在不同成像时间获得的不同图像,融合为一幅图像,在这一幅融合图像中能反映多重原始图像的信息,以达到对目标和场景的综合描述,使之更适合视觉感知或像医疗应用、目标检测与分类等计算机处理任务。它是一门综合了传感器、信号处理、图像处理和人工智能等技术的新兴学科分支。近些年来,多传感器图像融合己成为图像理解和计算机视觉领域中一项重要而有用的新技术。
多传感器图像融合的处理通常可在以下三个不同层次上进行：像素级、特征级、决策级。本文在分析国内外多传感器图像融合方法的基础上,主要对像素级和特征级融合技术以及多传感器图像融合评价方法等三个方面进行了研究,论文的主要学术贡献和工作如下：
(1)在像素级图像融合算法研究方面提出了三种算法。具体地有：提出一种新的基于窗体以及多目标向量求值的量子粒子群优化算法(VEQPSO)的图像融合方法。低频子带采用基于灰度关联分析的VEQPSO算法完成融合过程。高频部分划分窗体,根据不同的窗体类型,运用窗体信息能量和模糊熵指导融合策略；提出了一种改进的形态学小波多聚焦图像融合算法；针对形态学小波融合方法(MMWF)在重构尺度信号时由于发生位置错误而导致灰度值下溢的问题,采用了检测-重融合的方法,该方法保留了MMWF快速、有效、易于实现等优点,同时融合效果也得到了提高；针对统计模型的遥感图像多分辨率融合方法中需要设置相关门限以及约束条件、参数过多,使得算法复杂度增加的问题,提出一种改进的算法。该算法将约束条件变形,然后将其融合在目标函数中,构造新的目标函数,通过最大化拉格朗日函数求偏导数的方法估计参数。该算法可以避免原始算法中的参数设置,并且融合图像的空间分辨率和光谱保持能力均能达到较好的效果,算法鲁棒性增强并且复杂性降低。
(2)在多特征图像融合算法研究方面提出了四种算法。论文在研究基于多特征模糊聚类的图像融合方法的基础上,提出了基于卡尔曼滤波的噪声图像的融合方法。该方法结合了滤波和多特征的优点,可提高融合效果,减少图像噪声对融合的干扰；将多通道Gabor滤波与区域方法结合,提出了新的区域相似性度量方法用于图像融合。研究表明,该方法的融合性能对于Gabor滤波器参数(径向中心频率和方向角)的选取不敏感,算法具有一定的稳定性；针对FCM算法容易陷入局部最优的弱点,选用量子粒子群算法与模糊C均值聚类(FCM)相结合的方法(QPSO-FCM)。鉴于QPSO-FCM具有很好的分割效果,将全局优化算法QPSO引入多特征图像融合过程中,这将有利于融合效果的提高；提出一种新的基于二次融合多特征的融合方法,研究表明,这种二次融合方法对多聚焦图像融合有很好的效果。
(3)多传感器图像融合评价方面,在对经典的图像融合评价方法研究的基础上,提出了新的图像融合客观评价方法。考虑到人对区域信息更为敏感,因此算法将图像进行区域分割,利用区域特征矩阵表示区域中的空间、纹理和灰度信息等内容,算法更适合于评价。针对是否有参考图像,本文提出了两种用于评价融合图像的新的度量公式。此外,在上述研究的基础上将二维主成分分析(2DPCA)引入上述评价算法,又获得了一种新的算法；本文提出的图像融合评价新方法考虑了图像像素的局部关系以及区域的显著性,更加符合人类的视觉特征。
Multisensor image fusion is a kind of information fusion, which refers to the synergistic combination of different sources of sensory image into a single image. The information to be fused may come from different sensors of the same object or scene,or from the same sensor of different imaging manner, or from different images of different time period. The fused image can reflect multiple properties of source images, which makes it more suitable for the purpose of human visual perception and computer processing tasks such as medical applications, detection or classification tasks.Multisensor image fusion is a new branch of research that involves sensors, signal processing, image processing, and artificial intelligence. Recently, image fusion has been an important and useful technique for image analysis and computer vision.
Multisensor image fusion can occur at three different levels:pixel level, feature level, and decision level. However, the main concern of this thsis is to present a study on pixel level fusion, feature level fusion and the evaluation method of image fusion respectively. The main contributions are summarized as follows:
(1) Three new algorithms are proposed in pixel level fusion. The first new algorithm of pixel level fusion is proposed based on the combination of windows and vector evaluated quantum behaved particle swarm optimization(VEQPSO), in which VEQPSO incorprating with gray relational analysis is utilized for low frequency band, while information energy and fuzzy entropy of windows are adopted for fusion of high frequency band. Since there is position error which leads to the downward overflow of gray value of images when the scale signal is reconstructed for modified morphorlogical wavelet filter (MMWF), a detection-refusion strategy is proposed in the second proposed algorithm. The proposed method preserves the advantages of MMWF including fast speed, effectiveness, and easy implementation. Furthermore, the performance of the proposed fusion method is improved significantly. To reduce the dependence on parameter and strengthen the robustness of the algorithm based on statistical model, a modified statistical fusion model (MSFM) is proposed in which new objective function is presented and the constraints are simplified. Using this new model, the interrelated spatial information is well enhanced, and the spectral information of multi-spectral images is effectively kept. Furthermore, the proposed algorithm can not only avoid determining a threshod of the conventional statistical model, but the robustness is enhanced and the complexity is reduced as well.
(2) Four algorithms are proposed in feature level fusion. A noisy image fusion algorithm based on Kalman filter is proposed based on previous image fusion method using multi-feature fuzzy clustering. The proposed algorithm gains an improved performance since it combines the advantages of Kalman filter and multi-feature. A new region similarity is proposed based on multichannel Gabor filtering and region fusion method. The research indicates that the performance of image fusion is insensitive to the selection of different Gabor parameters including the center frequency and orientation. Furthermore, the proposed method for image fusion is stable in fusion performance. To avoid the local minimum problems of fuzzy C-means(FCM), a new clustering algorithm QPSO-FCM is proposed that incorporates the FCM into QPSO algorithm. Since QPSO-FCM can produce a very good segmentation, which leads to an improved performance of fusion. A new double fusion method based on multi-feature is proposed. The research indicates that the proposed method is very effective for the fusion of multi-focus images.
(3) After research on the conventional evaluation methods of image fusion, a novel metrics for evaluation of fused images are proposed based on the similarity of corresponding regions in images. The new metrics are computed on a region-by-region basis, which is more suitable for the evaluation because human eyes are more sensitive to regions. The region information is represented by feature matrix of region, which consists of multi-feature vectors including spatial information, texture and gray value, which can adequately reflect the regional content. Two new quality metrics are proposed which consider the reference image is available or not. Furthermore, two dimensional principal component analysis(2DPCA) is introduced into the process of evaluation, which leads to a new version of the proposed evaluation method. Research indicates that the proposed metrics are more consistent with the nature of human perception as it considers the local image variations and the saliency of region.
多传感器图像融合的处理通常可在以下三个不同层次上进行：像素级、特征级、决策级。本文在分析国内外多传感器图像融合方法的基础上,主要对像素级和特征级融合技术以及多传感器图像融合评价方法等三个方面进行了研究,论文的主要学术贡献和工作如下：
(1)在像素级图像融合算法研究方面提出了三种算法。具体地有：提出一种新的基于窗体以及多目标向量求值的量子粒子群优化算法(VEQPSO)的图像融合方法。低频子带采用基于灰度关联分析的VEQPSO算法完成融合过程。高频部分划分窗体,根据不同的窗体类型,运用窗体信息能量和模糊熵指导融合策略；提出了一种改进的形态学小波多聚焦图像融合算法；针对形态学小波融合方法(MMWF)在重构尺度信号时由于发生位置错误而导致灰度值下溢的问题,采用了检测-重融合的方法,该方法保留了MMWF快速、有效、易于实现等优点,同时融合效果也得到了提高；针对统计模型的遥感图像多分辨率融合方法中需要设置相关门限以及约束条件、参数过多,使得算法复杂度增加的问题,提出一种改进的算法。该算法将约束条件变形,然后将其融合在目标函数中,构造新的目标函数,通过最大化拉格朗日函数求偏导数的方法估计参数。该算法可以避免原始算法中的参数设置,并且融合图像的空间分辨率和光谱保持能力均能达到较好的效果,算法鲁棒性增强并且复杂性降低。
(2)在多特征图像融合算法研究方面提出了四种算法。论文在研究基于多特征模糊聚类的图像融合方法的基础上,提出了基于卡尔曼滤波的噪声图像的融合方法。该方法结合了滤波和多特征的优点,可提高融合效果,减少图像噪声对融合的干扰；将多通道Gabor滤波与区域方法结合,提出了新的区域相似性度量方法用于图像融合。研究表明,该方法的融合性能对于Gabor滤波器参数(径向中心频率和方向角)的选取不敏感,算法具有一定的稳定性；针对FCM算法容易陷入局部最优的弱点,选用量子粒子群算法与模糊C均值聚类(FCM)相结合的方法(QPSO-FCM)。鉴于QPSO-FCM具有很好的分割效果,将全局优化算法QPSO引入多特征图像融合过程中,这将有利于融合效果的提高；提出一种新的基于二次融合多特征的融合方法,研究表明,这种二次融合方法对多聚焦图像融合有很好的效果。
(3)多传感器图像融合评价方面,在对经典的图像融合评价方法研究的基础上,提出了新的图像融合客观评价方法。考虑到人对区域信息更为敏感,因此算法将图像进行区域分割,利用区域特征矩阵表示区域中的空间、纹理和灰度信息等内容,算法更适合于评价。针对是否有参考图像,本文提出了两种用于评价融合图像的新的度量公式。此外,在上述研究的基础上将二维主成分分析(2DPCA)引入上述评价算法,又获得了一种新的算法；本文提出的图像融合评价新方法考虑了图像像素的局部关系以及区域的显著性,更加符合人类的视觉特征。
Multisensor image fusion is a kind of information fusion, which refers to the synergistic combination of different sources of sensory image into a single image. The information to be fused may come from different sensors of the same object or scene,or from the same sensor of different imaging manner, or from different images of different time period. The fused image can reflect multiple properties of source images, which makes it more suitable for the purpose of human visual perception and computer processing tasks such as medical applications, detection or classification tasks.Multisensor image fusion is a new branch of research that involves sensors, signal processing, image processing, and artificial intelligence. Recently, image fusion has been an important and useful technique for image analysis and computer vision.
Multisensor image fusion can occur at three different levels:pixel level, feature level, and decision level. However, the main concern of this thsis is to present a study on pixel level fusion, feature level fusion and the evaluation method of image fusion respectively. The main contributions are summarized as follows:
(1) Three new algorithms are proposed in pixel level fusion. The first new algorithm of pixel level fusion is proposed based on the combination of windows and vector evaluated quantum behaved particle swarm optimization(VEQPSO), in which VEQPSO incorprating with gray relational analysis is utilized for low frequency band, while information energy and fuzzy entropy of windows are adopted for fusion of high frequency band. Since there is position error which leads to the downward overflow of gray value of images when the scale signal is reconstructed for modified morphorlogical wavelet filter (MMWF), a detection-refusion strategy is proposed in the second proposed algorithm. The proposed method preserves the advantages of MMWF including fast speed, effectiveness, and easy implementation. Furthermore, the performance of the proposed fusion method is improved significantly. To reduce the dependence on parameter and strengthen the robustness of the algorithm based on statistical model, a modified statistical fusion model (MSFM) is proposed in which new objective function is presented and the constraints are simplified. Using this new model, the interrelated spatial information is well enhanced, and the spectral information of multi-spectral images is effectively kept. Furthermore, the proposed algorithm can not only avoid determining a threshod of the conventional statistical model, but the robustness is enhanced and the complexity is reduced as well.
(2) Four algorithms are proposed in feature level fusion. A noisy image fusion algorithm based on Kalman filter is proposed based on previous image fusion method using multi-feature fuzzy clustering. The proposed algorithm gains an improved performance since it combines the advantages of Kalman filter and multi-feature. A new region similarity is proposed based on multichannel Gabor filtering and region fusion method. The research indicates that the performance of image fusion is insensitive to the selection of different Gabor parameters including the center frequency and orientation. Furthermore, the proposed method for image fusion is stable in fusion performance. To avoid the local minimum problems of fuzzy C-means(FCM), a new clustering algorithm QPSO-FCM is proposed that incorporates the FCM into QPSO algorithm. Since QPSO-FCM can produce a very good segmentation, which leads to an improved performance of fusion. A new double fusion method based on multi-feature is proposed. The research indicates that the proposed method is very effective for the fusion of multi-focus images.
(3) After research on the conventional evaluation methods of image fusion, a novel metrics for evaluation of fused images are proposed based on the similarity of corresponding regions in images. The new metrics are computed on a region-by-region basis, which is more suitable for the evaluation because human eyes are more sensitive to regions. The region information is represented by feature matrix of region, which consists of multi-feature vectors including spatial information, texture and gray value, which can adequately reflect the regional content. Two new quality metrics are proposed which consider the reference image is available or not. Furthermore, two dimensional principal component analysis(2DPCA) is introduced into the process of evaluation, which leads to a new version of the proposed evaluation method. Research indicates that the proposed metrics are more consistent with the nature of human perception as it considers the local image variations and the saliency of region.
引文
[I]POHL C, Van Genderen J L. Multisensor image fusion in remote sensing:concepts, methods and applications[J]. International Journal of Remote Sensing,1998,19(5):823-854
[2]敬忠良,等.图像融合——理论与应用[M].北京：高等教育出版社,2007.7-8
[3]I.Bloch,H.Maitre. Data fusion in 2D and 3D image processing:An overview[C]. in X Brazilian Symposium of Computer Graphic and Image Processing, Ed. L. H. de Fiqueiredo, and M. L. Netto, Campos de lordao SP, Oct.13-16,1997:127-134
[4]H.Maitre, I.Bloch. Image Fusion[J]. Vistas in Astronomy,1997,41(3):329-335
[5]T. Taxt, A.H. Schistad. Information fusion in remote sensing[J]. Vistas in Astronomy,1997,41(3): 337-342
[6]Z. Zhang, R. S. Blum. A categorization of multiscale decomposition-based image fusion schemes with a performance study for a digital camera application[C]. Proceedings of IEEE, vol.87, no.8, 1999.1315-1326
[7]G. Simone, A. Farina, F.C. Morabito et al. Image fusion techniques for remote sensing applications[J]. Information Fusion,2002,3:3-15
[8]K.Amolins, Y. Zhang, and P. Dare, Wavelet based image fusion techniques—an introduction, review and comparison[J]. Photogrammetry & Remote Sensing,2007,62(4):249-263
[9]Abhijit Sinha, Huimin Chen, D. G. Danu, et al. Estimation and decision fusion:A survey[J]. Neurocomputing,2008,71(13):2650-2656
[10]Yao J., Raghavan V.V., Wu Z.Web information fusion:A review of the state of the art[J]. Information Fusion,2008,9(4):446-449
[11]Ying-Ping Wang, Cathy M. Trudinger, Ian G. Enting. A review of applications of model-data fusion to studies of terrestrial carbon fluxes at different scales[J]. Agricultural and Forest Meteorology, 2009,149:1829-1842
[12]Igino Corona, Giorgio Giacinto, Claudio Mazzariello et al. Information fusion for computer security: State of the art and open issues[J]. Information Fusion,2009,10(4):274-284
[13]Twycross J., Aickelin U. Information fusion in the immune system[J]. Information Fusion, 2010,11(1):35-44
[14]毛士艺,赵巍等.多传感器图像融合技术综述[J].北京航空航天大学学报,2002,28(5)：512-518
[15]刘松涛,周晓东.图像融合技术研究的最新进展[J].激光与红外,2006,36(8)：627-631
[16]李敏,谈正,李晓燕.多传感器图像融合技术及评价方法研究[J].计算机工程与应用,2006,42(6)：39-41
[17]李伟.多源图像融合质量的综合评价体系研究[J].深圳信息职业技术学院学报,2008,6(2)：83-88
[18]张秀琼.融合图像质量评价综述[J].乐山师范学院学报,2008,23(12)：62-65
[19]储彬彬,庞璐璐.基于多尺度分析的图像融合技术综述[J].航空电子技术,2009,40(3)：29-33
[20]胡钢,刘哲等.像素级图像融合技术的研究与进展[J].计算机应用研究,2008,25(3)：650-655
[21]卢森宗.像素级遥感图像融合方法研究[J].现代测绘,2009,32(3)：24-26
[22]乔闹生,张黎,王先春.遥感图像融合方法研究[J].计算机工程与应用,2009,45(8)：182-189
[23]柴勇,何友,曲长文.遥感图像融合最新进展及展望[J].舰船电子工程,2009,29(8)：1-5
[24]朱卫纲,周荫清等.遥感图像融合效果客观评价[J].火力与指挥控制,2010,35(2)：1-5
[25]王江涛.医学图像融合的临床应用与新进展[J].医学影像学杂志,2009,19(4)：476-478
[26]张季涛.图像融合在武器装备中的作用[J].应用光学,1999,20(3)：1-3
[27]崔麦会,周建军,陈超.面向军事应用的多传感器图像融合技术发展研究[J],国防技术基础,2005(4)：4-5
[28]Yifeng zhou, Henry Leung. An exact maximum likelihood registration algorithm for data fusion[J].IEEE TRANSACTIONS ON SIGNAL PROCESSING,1997,45(60):1560-1573
[29]Olivier TheApaut, Kidiyo Kpalma, Joseph Ronsin. Automatic registration of ERS and SPOT multisensor images in a data fusion context[J].Forest Ecology and Management,2000,(128):93-100
[30]Zhong Zhang, Rick S. Blum. A hybrid image registration technique for a digital camera image fusion application[J].Information fusion,2001(2):135-149
[31]France Lalibert, Langis Gagnon, Yunlong Sheng. Registration and Fusion of Retinal images:A Comparative Study[C]. IEEE Conference on Pattern Recognition. Auguest,2002.715-718
[32]Winston Li, Henry Leung. Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2004,5(2):84-98
[33]Markus Muller, Wolfgang Kruger, Gunter Saur. Robust image registration for fusion[J]. Information Fusion,2007,8(4):347-353
[34]曾文锋.红外与可见光图像融合中的快速配准方法[J].红外与激光工程,2002,31(2)：158-160
[35]刘斌,彭嘉雄.图像配准的小波分解方法[J].计算机辅助设计与图形学学报,2003,15(9)：1070-1073
[36]于秋则,田金文,柳健.基于边缘正则化小波描述的多传感器图像自动配准研究[J].信号处理,2005,21(1)：35-39
[37]陈木生.基于区域特征的图像配准方法研究[J].红外技术,2010,32(3)：145-151
[38]P.S. Chavez, A. Y. Kwarteng. Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis[J]. Photogramm. Eng. Remote Sensing,1989,55(3): 339-348
[39]W. J. Carper, T. M. Lillesand, R.W. Kiefer. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data[J]. Photogramm. Eng. Remote Sensing, 1990,56(4):459-467
[40]P. S. Chavez Jr., S. C. Sides, J. A. Anderson. Comparison of three difference methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic[J]. Photogramm. Eng. Remote Sensing,1991,57(3):295-303
[41]Tu T. M., Su S. C., Shyu H. C. et al. A new look at IHS-like image fusion methods[J]. Information Fusion,2001,2(3):177-186
[42]C. M. Chen, G.F. Hepner, R.R. Forster. Fusion of hyperspectral and radar data using the HIS transformation to enhance urban surface features[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2003,58 (1):19-30
[43]Yun Zhang, Gang Hong. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J]. Information Fusion,2003,6(3):225-234
[44]Yangrong Ling, Manfred Ehlers, E. Lynn Usery et al. FFT-enhanced IHS transform method for fusing high-resolution satellite images[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2007, 61(6):381-392
[45]Sabalan Daneshvar,Hassan Ghassemian. MRI and PET image fusion by combining IHS and retina-inspired models[J]. Information Fusion,2010,11 (2):114-123
[46]Nikolaos Mitianoudis, Tania Stathaki. Adaptive image fusion using ICA bases[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.2,2006.829-832
[47]Jing Sui, Tulay Adali, Godfrey Pearlson et al. A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia[J]. NeuroImage,2010,51 (1):123-134
[48]玉振明,毛士艺,高飞.采用局部谐波能量的图像融合准则研究[J].电子学报,2004,32(6)：890-894
[49]胡冰,周海芳,王攀峰,刘衡竹.遥感图像PCA融合的并行算法研究与实现[J].微电子学与计算机,2006,23(10)：153-155
[50]吴学明,杨武年.基于2DPCA的遥感图像融合算法研究[J].地质科技情报,2008,27(5)：97-101
[51]蒋年德,王耀南.一种新的基于主分量变换与小波变换的图像融合方法[J].中国图像图形学 报,2005,10(7)：910-915
[52]于森,陈春香.一种基于KPCA与小波变换的遥感图像融合算法[J].桂林工学院学报,2009,29(4)：555-560
[53]李波,吕海莲.基于累加梯度和PCA变换的图像融合算法[J].2010,31(3)：574-576
[54]何贵青郝重阳.特征量积和IHS变换的多源遥感图像融合方法[J].火力与指挥控,2006,31(11)：88-90
[55]李光鑫,王珂.基于Contourlet变换的彩色图像融合算法[J].电子学报,2007,35(1)：112-117
[56]王加,蒋晓瑜,纪伯公.基于感知颜色空间的灰度可见光与红外图像融合算法[J].光电子.激光,2008,19(9)：1261-1264
[57]郑有志,覃征.基于二维经验模态分解的医学图像融合算法[J].软件学报,2009,20(5)：1096-1105
[58]孙俊平,刘扬.基于快速独立分量分析的多分辨率遥感图像融合算法[J].中国电子科学研究院学报,2007,2(3)：228-233
[59]Toet A. Image fusion by a ratio of low-pass pyramid [J]. Pattern Recognition Letters,1989,9(4): 245-253
[60]Toet A, Ruyven L J, Valeton J M. Merging thermal and visual images by a contrast pyramid [J]. Optical Engineering,1989,28(7):789-792
[61]Toet A. Multiscale contrast enhancement with applications to image fusion [J]. Optical Engineering, 1992,31(5):1026-1031
[62]Toet A. A morphological pyramidal image decomposition [J]. Pattern Recognition Letters,1989,9(4): 255-261
[63]G. K. Matsopoulos, S. Marshall, J. N. H. Brunt. Multiresolution morphological fusion of MR and CT images of the human brain[C]. Proceedings, of IEE, Vision, Image and Signal Processing,141(3), 1994.137-142
[64]Susanta Mukhopadhyay, Bhabatosh Chanda. Fusion of 2D grayscale images using multiscale morphology [J]. Pattern Recognition,2001(34):1939-1949
[65]Z. Liu, K. Tsukada, K. Hanaski et al. Image fusion by using steerable pyramid[J]. Pattern Recognition Letters,2001 (22):929-939
[66]Ishita De, Bhabatosh Chanda, Buddhajyoti Chattopadhyay. Enhancing effective depth-of-field by image fusion using mathematical morphology [J]. Image and Vision Computing,2006,24(12): 1278-1287
[67]Ishita De, Bhabatosh Chanda. A simple and efficient algorithm for multifocus image fusion using morphological wavelets[J]. Signal Processing,2006,86 (5):924-936
[68]Phen-Lan Lina, Po-Ying Huang. Fusion methods based on dynamic-segmented morphological wavelet or cut and paste for multifocus images[J]. Signal Processing,2008,88(6):1511-1527
[69]R. Redondo, F. Sroubek b, S. Fischer et al. Multifocus image fusion using the log-Gabor transform and aMultisize Windows technique[J]. Information Fusion,2009,10 (2):163-171
[70]Nikoukhah B R, Willsky A S. Multiscale system theory [A]. In:Proc 29th IEEE Conference on Decision and Control [C],1990.2484-2487
[71]Simone G, Morabito F C, Farina A. Radar image fusion by multiscale Kalman filtering [A]. In:Proc of the 3rd International Conference on Information Fusion[C],2,2000.10-17.
[72]J. M. Laferte, F. Heitz, P. Perez, E. Fabre. Hierarchical statistical models for the fusion of multiresolution image data[A], Fifth International Conference on Computer Vision (ICCV'95)[C], 1995:908-913
[73]Costantini M, Farina A, Zirilli F. The Fusion of Different Resolution SAR Images [A]. Proceedings of the IEEE[C],85(1),1997.139-146
[74]蒲恬,倪国强.应用于图像融合中的多尺度对比度调制法[J].北京理工大学学报,2000,20(2)：219-223
[75]李玲玲,周成平,丁明跃等.一种基于多尺度分解的多传感器图像融合方法[J].华中科技大学学报(自然科学版),2003,31(4)：82-84
[76]徐宝昌,陈哲.基于PCNN的多尺度对比度塔图像融合算法[J].计算机工程与应用,2004,21(10)：14-17
[77]张大明,阮祥伟,胡茂林.基于金字塔方法的SAR图像与光学图像的融合[J].雷达科学与技术,2005,3(6)：350-355
[78]朱瑞辉,范国滨,万敏.一种基于多分辨率分析的图像融合方法[J].光学与光电技术,2007,5(5)：82-84
[79]钱永浩,吴小俊,罗晓清.改进的基于形态学小波多聚焦图像融合方法[J].计算机工程与应用,2010,46(7)：158-161
[80]Chipman L J, Orr T M, Graham L N. Wavelets and image fusion [C]. Proc of Int Conf on Image Processing. Los Alamitos:IEEE Computer Society,1995.248-251
[81]Li H, Manjunath B S, Mitra S. Multisensor image fusion using the wavelet transform [J]. Graphical Models and Image Process,1995,57(3):235-245
[82]Hill P, Canagarajah N, Bull D. Image fusion using complex wavelets[C]. Proceedings of the British Machine Conference. Cardiff, UK,2002.487-496
[83]Youcef Chibani, Amrane Houacine. Redundant versus orthogonal wavelet decomposition for multisensor image fusion[J]. Pattern Recognition,2003,36 (4):879-887
[84]Gonzalo Pajares, Jesus Manuel de la Cruz. A wavelet-based image fusion tutorial[J]. Pattern Recognition,2004,37 (9):1855-1872
[85]Zhenhua Li, Zhongliang Jing, Xuhong Yang et al. Color transfer based remote sensing image fusion using non-separable wavelet frame transform[J]. Pattern Recognition Letters,2005,26 (13): 2006-2014
[86]Shutao Li, Bin Yang. Multifocus image fusion by combining curvelet and wavelet transform[J]. Pattern Recognition Letters,2008,29(9):1295-1301
[87]Chen Shao-hui, Su Hongbo, Zhang Renhua. Fusing remote sensing images using a trous wavelet transform and empirical mode decomposition[J]. Pattern Recognition Letters,2008,29(9):330-342
[88]Qiang Zhang, Bao-long Guo. Multifocus image fusion using the nonsubsampled contourlet transform[J]. Signal Processing,2009,89(7):1334-1346
[89]Shuyuan Yang, Min Wang, Licheng Jiao et al. Image fusion based on a new contourlet packet[J]. Information Fusion,2010(11):78-84
[90]Shutao Li, Bin Yang, Jianwen Hu. Performance comparison of different multi-resolution transforms for image fusion[J]. Information Fusion,2010:1-11
[91]王蓉,高立群,柴玉华等.一种多聚焦图像融合方法[J].控制与决策,2005,20(11)：1256-1260
[92]贾建,焦李成,孙强.基于非下采样Contourlet变换的多传感器图像融合[J].电子学报,2007,35(10)：1934-1938
[93]杨晓慧,金海燕,焦李成.基于DT-CWT的红外与可见光图像自适应融合[J].红外与毫米波学报,2007,26(6)：419-424
[94]杨风暴,倪国强,张雷.红外中波细分图像的小波包变换融合研究[J].红外与毫米波学报,2008,27(4)：275-279
[95]马艳军,徐立中,汤敏.遥感图像融合的非采样Contourlet变换方法[J].中国图象图形学报,2008,13(11)：2209-2216
[96]时海亮,方敏,梁锦锦.基于非亚采样Contourlet和SWT的多光谱图像和全色图像的融合算法[J].电子与信息学报,2009,31(1)：124-128
[97]金海燕,焦李成.基于CP和多小波HMT模型的克隆选择遥感图像融合[J].计算机学报,2009,32(7)：1434-1442
[98]刘斌,彭嘉雄.基于四通道不可分加性小波的多光谱图像融合[J].计算机学报,2009,32(2)：350-356
[99]龚建周,刘彦随,夏北成等.小波基及其参数对遥感影像融合图像质量的影响[J].地理与地理信息科学,2010,26(2)：6-10
[100]Gemma Piella. A general framework for multiresolution image fusion:from pixels to regions[J]. Information Fusion,2003,4 (4):259-280
[101]S. G. Nikolov, J. J. Lewis, R. J. O'Callaghan et al. Hybrid fused displays:Between pixel- and region-based image fusion[A]. Proceedings of the Seventh International Conference on Information Fusion[C],2,2004.1072-1079
[102]J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov. Pixel- and region-based image fusion with complex wavelets[J]. Information Fusion,2007,8(2):119-130
[103]Shutao Li, Bin Yang. Multifocus image fusion using region segmentation and spatial frequence [J]. Image and Vision Computing,2008,26(7):971-979
[104]Liu Kun, Guo Lei, Li Huihui et al. Fusion of Infared and Visible light images based on region segmentation[J]. Chinese Journal of Aeronautics,2009,22(1):75-80
[105]李晖晖,郭雷,刘航.基于区域分割的遥感图像融合方法[J].光子学报,2005,34(12)：1901-1905
[106]苏冬雪,吴小俊.基于区域颜色校正的遥感图像融合方法[J].江苏科技大学学报(自然科学版),2006,20(2)：60-64
[107]苏冬雪,吴小俊.基于多特征模糊聚类的图像融合方法[J].计算机辅助设计与图形学学报,2006,18(6)：838-843
[108]冯舒,蒋宏,任章.基于多分辨率分割的区域图像融合[J].计算机仿真,2007,24(5)：183-185
[109]岳晋,宦若虹,上官晋太.一种新的ICA域图像融合算法[J].光电工程,2008,35(5)：129-134
[110]叶传奇,王宝树,苗启广.一种基于区域特性的红外与可见光图像融合算法[J].光子学报,2009,38(6)：1498-1502
[111]Borotschnig H., Pinz A.. A new concept for active fusion in image understanding applying fuzzy set theory[C]. In Proceedings 5th IEEE International Conference on Fuzzy Systems 1996 (FUZZ-IEEE '96), New Orleans, volume 2,1996.793-799
[112]Prantl M., Ganster H., Pinz A.. Active fusion using bayesian networks applied to multi-temporal remote sensing imagery[C]. In Proceedings of the 13th ICPR, volume Ⅲ，1996.890-894
[113]Pinz A., Prantl M., Ganster H. et al. Active fusion-A new method applied to remote sensing image interpretation[J]. Pattern Recognition Letters,1996,17 (13):1349-1359
[114]M.-P. Dubuisson-Jolly, A. Gupta. Color and texture fusion:application to aerial image segmentation and GIS updating[J]. Image and Vision Computing,2000,18:823-832
[115]D.A.,Deng, H.. Fusion of Gabor filter and co-occurrence probability features for texture recognition[C]. IEEE Transactions on Image Processing, Vol.14, No.7,2005.925-936
[116]Yan Wu, Ming Li, Guisheng Liao. Multiple features data fusion method in color texture analysis[J]. Applied Mathematics and Computation,2007,185(2):784-797
[117]张易凡,何明一.综合有向纹理特征及其在多光谱图像融合中的应用[J].电子与信息学报,2007,29(1)：81-86
[118]王志慧,赵保军,冯永霖.基于特征提取和脉冲耦合神经网络的图像融合算法[J].光学技术,2009,35(4)：510-516
[119]P.Bogler. Shafer-Dempster reasoning with applications to multisensor target identification [J]:IEEE Trans, on System, Man, and Cybernetics,1987,6 (12):968-977
[120]Sateesha G.,Nadabar,Anil K. Jain.Fusion of range and intensity images on a connection machine(CM-2)[J]. Pattern Recognition,1995,28(1):11-26
[121]Fay D A, Waxman A M, Verly J G, et al. Fusion of visible, infrared and 3D LADAR imagery [A]. In: Proc of the 4th International Conference on Information Fusion[C], Montreal, SPIE-4363,2001. 219-230
[122]Sansanee Auephanwiriyakul, James M. Keller, Paul D. Gader. Generalized Choquet fuzzy integral fusion[J]. Information Fusion,2002,3(1):69-85
[123]Min Li, Wei Cai, Zheng Tan. A region-based multi-sensor image fusion scheme using pulse-coupled neural network[J]. Pattern Recognition Letters,2006,27(16):1948-1956
[124]Wei Huang, Zhongliang Jing. Multi-focus image fusion using pulse coupled neural network[J]. Pattern Recognition Letters,2007,28(9):1123-1132
[125]Zhaobin Wang, Yide Ma. Medical image fusion using m-PCNN[J]. Information Fusion,2008,9 (2):176-185
[126]Alaeddin Malek, MaryamYashtini. Image fusion algorithms for color and gray level images based on LCLS method and novel artificial neural network[J]. Neurocomputing,2010,73 (4):937-943
[127]杨烜,裴继红,杨万海.基于模糊积分的融合图像评价方法[J].计算机学报,2001,24(8)：815-818
[128]刘国良,强文义.基于粗神经网络的图像融合算法的研究[J].模式识别与人工智能,2003,16(3)：370-373
[129]鲍复民,李爱国,覃征.基于SGNN的图像融合[J].计算机研究与发展,2005,42(3)：417-423
[130]苗启广,王宝树.一种自适应PCNN多聚焦图像融合新方法[J].电子与信息学报, 2006,28(3)：466-470
[131]苗启广,王宝树.基于局部对比度的自适应PCNN图像融合[J].计算机学报,2008,31(5)：875-880
[132]Paul Scheunders. Multispectral image fusion using local mapping techniques[A]. in:Proceedings ICPR2000[C], Barcelona, Spain, vol.2,2000.2311-2315
[133]Farid Melgani, Sebastiano B. Serpico. A statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing images[J]. Pattern Recognition Letters,2002,23 (1):1053-1061
[134]Eloi Bosse, Pierre Valin et al. Exploitation of a priori knowledge for information fusion[J]. Information Fusion,2006,7(2):161-175
[135]R. Redondo, S. Fischer, F. S roubek. A 2D Wigner Distribution-based multisize windows technique for image fusion[J]. J. Visual Commun. Image Represent,2008,19(1):12-19
[136]陈洪波,王强,张孝飞等.基于小波系数邻域特征的图像融合[J].光学精密工程.2003,11(5)：516-522
[137]郭若杉,陈涛,彭思龙.基于局部线模型的彩色IC图像融合[J].中国图象图形学报.2005,10(4)：450-456
[138]龚昌来.一种改进的基于局部能量图像融合算法[J].光电工程.2008,35(11)：106-110
[139]曾立庆,蒋年德.基于邻域内相关系数与平均梯度的图像融合方法[J].计算机工程与设计.2010,31(7)：1533-1539
[140]Yufeng Zheng, Edward A Essock, Bruce C Hansen, Andrew M Haun. A new metric based on extended spatial frequency and its application to DWT based fusion algorithms [J]. Information Fusion,2007,8(2):177-192
[141]Wei Huang, Zhong Liangjing. Evaluation of focus measures in multi-focus image fusion [J]. Pattern Recognition Letters,2007,28(4):493-500
[142]Qu G, Zhang D, Yan P. Information measure for performance of image fusion [J]. Electronics Letters,2002,38(7):313-315
[143]Wang Z, Bovik AC. A universal image quality index [J]. IEEE Signal Processing Letters,2002,9(3): 81-84
[144]G Piella, H Heijmans. A new quality metric for image fusion [A], in Proceedings of IEEE:Image Processing [C]. Barcelona, Spain:IEEE Press,2003.173-176
[145]Wenzhong Shi, ChangQing Zhu, Yan Tian et al. Wavelet-based image fusion and quality assessment [J]. International Journal of Applied Earth Observation and Geoinformation,2005,6: 241-251
[146]Yufeng Zheng a, Edward A. Essock, Bruce C. Hansen et al. A new metric based on extended spatial frequency and its application to DWT based fusion algorithms [J]. Information Fusion,2007, 8(2):177-192
[147]Cui Yang, Jian-Qi Zhang, Xiao-Rui Wang et al. A novel similarity based quality metric for image fusion[J]. Information Fusion,2008,9(2):156-160
[148]Yin Chen, Zhiyun Xue, Rick S. Blum. Theoretical analysis of an information-based quality measure for image fusion[J]. Information Fusion,2008,9(2):161-175
[149]Yin Chen, Rick S. Blum. A new automated quality assessment algorithm for image fusion[J]. Image and Vision Computing,2009,27:1421-1432
[150]Chuanming Wei, Rick S. Blum. Theoretical analysis of correlation-based quality measures for weighted averaging image fusion[J]. Information Fusion,2010,11 (2):301-310
[151]Xiao-Qing Luo, Xiao-Jun WU.A New Metric of Image Fusion Based on Region SimilarityfJ]. Optical Engineering,2010,49(4):047006-1-047006-13
[152]胡良梅,高隽,何柯峰.图像融合质量评价方法的研究[J].电子学报,2004,32(12)：218-221
[153]王超,叶中付.基于相似性的图像融合质量的客观评估方法[J].软件学报,2006,7(7)：1580-15587
[154]何贵青,陈世浩,田沄等.多传感器图像融合效果综合评价研究[J].计算机学报,2008,31(3)：486-492
[155]Rick S.Blum, and Zheng Liu. Multi-Sensor Image Fusion and Its Applications[M],Taylor & Francis, Florida,USA,2006
[156]王润生.信息融合[M],科学出版社,2007年9月
[157]邓聚龙.灰色系统基本方法[M].武汉：华中理工大学出版社,1996
[158]Sun J, Xu W B. A global search strategy of quantum-behaved particle swarm optimization[C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems Singapore,2004.111-116
[159]S.N. Omkar, Rahul Khandelwal, T.V.S. Ananth et al. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures[J]. Expert Systems with Applications,2009,36:11312-11322
[160]徐锬,王超.基于模糊数学的数字图像模糊度[J].计算机辅助设计与图形学学报,2002,14(8)：747-749
[161]苏冬雪.图像融合理论及其应用的研究[D].[硕士学位论文],江苏科技大学,2005
[162]敬忠良,肖刚,李振华.图像融合--理论与应用[M].高等教育出版社,2007.
[163]H.j.Heijimans, j.Goutsias, Multi-resolution signal decomposition schemes. Part 2:Morphological wavelets[C], IEEE Transaction on Image processing,9,2000.1897-1913
[164]佘二永,王润生,徐学文.基于统计模型的遥感图像多分辨率融合方法[J],电子学报,2008,36(3)：490-493
[165]W. Cao, B. Li, and Y. Zhang, A remote sensing image fusion method based on PCA transform and wavelet packet transform[A]. In:Proc. IEEE Int. Conf. Neural Networks & Signal Processing[C], Nanjing, China, vol.14, Dec.2003.976-981
[166]邓自立Kalman滤波与维纳滤波——现代时间序列分析方法[M],哈尔滨：哈尔滨工业大学出版社,2001年7月
[167]Yixin Chen, James Z. Wang. A region-based fuzzy feature matching approach to content-based image retrieval[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(9): 1252-1267
[168]吴体瑞,吴小俊.一种基于区域和Contourlet变换的多聚焦图像融合新方法[J].中国体视学与图像分析,2007,12(2)：98-104
[169]章毓晋.中国图像工程及当前的几个研究热点[J].计算机辅助设计与图形学学报,2002,14(6)：489-500
[170]陆丽珍.基于多通道Gabor纹理特征的遥感图像检索[J].浙江大学学报(理学版),2004,31(6)：708-711
[171]焦峰.基于局部特征分析的人脸识别方法[J].计算机辅助设计与图形学学报,2003,15(1)：53-58
[172]朱勇,谭铁牛,王蕴红.基于笔迹的身份鉴别[J].自动化学报,2001,27(2)：229-234
[173]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别[J].自动化学报,2002,28(1)：1-10
[174]Xiao-Jun WU, Dong-Xue Su, Xiao-Qing Luo. A New Similarity Function for Region Based Image Fusion Incorporating with Gabor Filters and Fuzzy c-means clustering[C], SPIE,vol.6625,2008. 66250Z.1-66250Z.10
[175]Xiaojun Wu, Dongxue Su,Xiaoqing Luo. Multi-feature method for region based image fusion[C], Proceedings of Asigraph, Toyko, Japan, Digital Content Association of Japan(DCAJ),Dec.2008
[176]Jain A K, Prabhakar S, Hong L, et al. Filterbank-based fingerprint matching[J]. IEEE Transactions on Image Processing,2000,9(5):846-859
[177]覃征,等.数字图像融合[M].西安交通大学出版社,2004：92-100
[178]金剑秋,等.多光谱图象的真实感融合[J].中国图象图形学报,2002,7A(9)：926-931
[179]Sun J, Feng B, Xu W B.Particle Swarm Optimization with Particles Having Quantum Behavior[C], Proceedings of 2004 Congress on Evolutionary Computation,2004.325-331
[180]Hao Wang, Shiqin Yang, Jun Sun et al. Scalability of hybrid fuzzy c-means algorithm based on quantum-behaved PSO[C], Fourth international Conference on Fuzzy Systems and Knowledge Discovery, FSKD (2) 2007.261-265
[181]R. Hong and F. Sun, Structure similarity based objective metric for pixel-level image fusion[C],in Proc. The 2nd International Symposium on Systems and Control in Aerospace and Astronautics, Shenzhen, China,2008.1-6
[182]Liu K, Cheng Y Q, Yang J Y, et al. Algebraic feature extraction for image recognition based on an optimal discriminant criterion[J]. Pattern Recognition,1993,26 (6):903-911
[183]J Yang, D Zhang, J Y Yang, et al.. Two-dimensional PC A:A New Approach to Appearance-based Face Representation and Recognition [J]. IEEE Transactions on PAMI,2004,26(1):131-137
[184]Cheng H D, Chen J R, Li J. Threshold selection based on fuzzy c-partition entropy approach[J]. Pattern Recognition,1998,31(7):857-870
[185]刘贵喜,杨万海.基于小波分解的图像融合方法及性能评价[J].自动化学报,2002,28(6)：927-934
[186]C.S. Xydeas and V. Petrovic. Objective Image Fusion Performance Measure [J].Electronics Letters, 2000,36(4):306-309
[187]Shutao Li, Bin Yang. Multifocus image fusion using region segmentation and spatial frequency [J]. Image and Vision Computing,2008,26 (7):971-979
[188]F. Jahard, D.A. Fish, A.A. Rio and C.P. Thompson. Far/near infrared adapted pyramid-based fusion for automotive night vision[C], in Proc. IEEE 6th Int. Conf. on Image Processing and its Applications (IPA97),1997.886-890
[2]敬忠良,等.图像融合——理论与应用[M].北京：高等教育出版社,2007.7-8
[3]I.Bloch,H.Maitre. Data fusion in 2D and 3D image processing:An overview[C]. in X Brazilian Symposium of Computer Graphic and Image Processing, Ed. L. H. de Fiqueiredo, and M. L. Netto, Campos de lordao SP, Oct.13-16,1997:127-134
[4]H.Maitre, I.Bloch. Image Fusion[J]. Vistas in Astronomy,1997,41(3):329-335
[5]T. Taxt, A.H. Schistad. Information fusion in remote sensing[J]. Vistas in Astronomy,1997,41(3): 337-342
[6]Z. Zhang, R. S. Blum. A categorization of multiscale decomposition-based image fusion schemes with a performance study for a digital camera application[C]. Proceedings of IEEE, vol.87, no.8, 1999.1315-1326
[7]G. Simone, A. Farina, F.C. Morabito et al. Image fusion techniques for remote sensing applications[J]. Information Fusion,2002,3:3-15
[8]K.Amolins, Y. Zhang, and P. Dare, Wavelet based image fusion techniques—an introduction, review and comparison[J]. Photogrammetry & Remote Sensing,2007,62(4):249-263
[9]Abhijit Sinha, Huimin Chen, D. G. Danu, et al. Estimation and decision fusion:A survey[J]. Neurocomputing,2008,71(13):2650-2656
[10]Yao J., Raghavan V.V., Wu Z.Web information fusion:A review of the state of the art[J]. Information Fusion,2008,9(4):446-449
[11]Ying-Ping Wang, Cathy M. Trudinger, Ian G. Enting. A review of applications of model-data fusion to studies of terrestrial carbon fluxes at different scales[J]. Agricultural and Forest Meteorology, 2009,149:1829-1842
[12]Igino Corona, Giorgio Giacinto, Claudio Mazzariello et al. Information fusion for computer security: State of the art and open issues[J]. Information Fusion,2009,10(4):274-284
[13]Twycross J., Aickelin U. Information fusion in the immune system[J]. Information Fusion, 2010,11(1):35-44
[14]毛士艺,赵巍等.多传感器图像融合技术综述[J].北京航空航天大学学报,2002,28(5)：512-518
[15]刘松涛,周晓东.图像融合技术研究的最新进展[J].激光与红外,2006,36(8)：627-631
[16]李敏,谈正,李晓燕.多传感器图像融合技术及评价方法研究[J].计算机工程与应用,2006,42(6)：39-41
[17]李伟.多源图像融合质量的综合评价体系研究[J].深圳信息职业技术学院学报,2008,6(2)：83-88
[18]张秀琼.融合图像质量评价综述[J].乐山师范学院学报,2008,23(12)：62-65
[19]储彬彬,庞璐璐.基于多尺度分析的图像融合技术综述[J].航空电子技术,2009,40(3)：29-33
[20]胡钢,刘哲等.像素级图像融合技术的研究与进展[J].计算机应用研究,2008,25(3)：650-655
[21]卢森宗.像素级遥感图像融合方法研究[J].现代测绘,2009,32(3)：24-26
[22]乔闹生,张黎,王先春.遥感图像融合方法研究[J].计算机工程与应用,2009,45(8)：182-189
[23]柴勇,何友,曲长文.遥感图像融合最新进展及展望[J].舰船电子工程,2009,29(8)：1-5
[24]朱卫纲,周荫清等.遥感图像融合效果客观评价[J].火力与指挥控制,2010,35(2)：1-5
[25]王江涛.医学图像融合的临床应用与新进展[J].医学影像学杂志,2009,19(4)：476-478
[26]张季涛.图像融合在武器装备中的作用[J].应用光学,1999,20(3)：1-3
[27]崔麦会,周建军,陈超.面向军事应用的多传感器图像融合技术发展研究[J],国防技术基础,2005(4)：4-5
[28]Yifeng zhou, Henry Leung. An exact maximum likelihood registration algorithm for data fusion[J].IEEE TRANSACTIONS ON SIGNAL PROCESSING,1997,45(60):1560-1573
[29]Olivier TheApaut, Kidiyo Kpalma, Joseph Ronsin. Automatic registration of ERS and SPOT multisensor images in a data fusion context[J].Forest Ecology and Management,2000,(128):93-100
[30]Zhong Zhang, Rick S. Blum. A hybrid image registration technique for a digital camera image fusion application[J].Information fusion,2001(2):135-149
[31]France Lalibert, Langis Gagnon, Yunlong Sheng. Registration and Fusion of Retinal images:A Comparative Study[C]. IEEE Conference on Pattern Recognition. Auguest,2002.715-718
[32]Winston Li, Henry Leung. Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2004,5(2):84-98
[33]Markus Muller, Wolfgang Kruger, Gunter Saur. Robust image registration for fusion[J]. Information Fusion,2007,8(4):347-353
[34]曾文锋.红外与可见光图像融合中的快速配准方法[J].红外与激光工程,2002,31(2)：158-160
[35]刘斌,彭嘉雄.图像配准的小波分解方法[J].计算机辅助设计与图形学学报,2003,15(9)：1070-1073
[36]于秋则,田金文,柳健.基于边缘正则化小波描述的多传感器图像自动配准研究[J].信号处理,2005,21(1)：35-39
[37]陈木生.基于区域特征的图像配准方法研究[J].红外技术,2010,32(3)：145-151
[38]P.S. Chavez, A. Y. Kwarteng. Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis[J]. Photogramm. Eng. Remote Sensing,1989,55(3): 339-348
[39]W. J. Carper, T. M. Lillesand, R.W. Kiefer. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data[J]. Photogramm. Eng. Remote Sensing, 1990,56(4):459-467
[40]P. S. Chavez Jr., S. C. Sides, J. A. Anderson. Comparison of three difference methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic[J]. Photogramm. Eng. Remote Sensing,1991,57(3):295-303
[41]Tu T. M., Su S. C., Shyu H. C. et al. A new look at IHS-like image fusion methods[J]. Information Fusion,2001,2(3):177-186
[42]C. M. Chen, G.F. Hepner, R.R. Forster. Fusion of hyperspectral and radar data using the HIS transformation to enhance urban surface features[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2003,58 (1):19-30
[43]Yun Zhang, Gang Hong. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J]. Information Fusion,2003,6(3):225-234
[44]Yangrong Ling, Manfred Ehlers, E. Lynn Usery et al. FFT-enhanced IHS transform method for fusing high-resolution satellite images[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2007, 61(6):381-392
[45]Sabalan Daneshvar,Hassan Ghassemian. MRI and PET image fusion by combining IHS and retina-inspired models[J]. Information Fusion,2010,11 (2):114-123
[46]Nikolaos Mitianoudis, Tania Stathaki. Adaptive image fusion using ICA bases[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.2,2006.829-832
[47]Jing Sui, Tulay Adali, Godfrey Pearlson et al. A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia[J]. NeuroImage,2010,51 (1):123-134
[48]玉振明,毛士艺,高飞.采用局部谐波能量的图像融合准则研究[J].电子学报,2004,32(6)：890-894
[49]胡冰,周海芳,王攀峰,刘衡竹.遥感图像PCA融合的并行算法研究与实现[J].微电子学与计算机,2006,23(10)：153-155
[50]吴学明,杨武年.基于2DPCA的遥感图像融合算法研究[J].地质科技情报,2008,27(5)：97-101
[51]蒋年德,王耀南.一种新的基于主分量变换与小波变换的图像融合方法[J].中国图像图形学 报,2005,10(7)：910-915
[52]于森,陈春香.一种基于KPCA与小波变换的遥感图像融合算法[J].桂林工学院学报,2009,29(4)：555-560
[53]李波,吕海莲.基于累加梯度和PCA变换的图像融合算法[J].2010,31(3)：574-576
[54]何贵青郝重阳.特征量积和IHS变换的多源遥感图像融合方法[J].火力与指挥控,2006,31(11)：88-90
[55]李光鑫,王珂.基于Contourlet变换的彩色图像融合算法[J].电子学报,2007,35(1)：112-117
[56]王加,蒋晓瑜,纪伯公.基于感知颜色空间的灰度可见光与红外图像融合算法[J].光电子.激光,2008,19(9)：1261-1264
[57]郑有志,覃征.基于二维经验模态分解的医学图像融合算法[J].软件学报,2009,20(5)：1096-1105
[58]孙俊平,刘扬.基于快速独立分量分析的多分辨率遥感图像融合算法[J].中国电子科学研究院学报,2007,2(3)：228-233
[59]Toet A. Image fusion by a ratio of low-pass pyramid [J]. Pattern Recognition Letters,1989,9(4): 245-253
[60]Toet A, Ruyven L J, Valeton J M. Merging thermal and visual images by a contrast pyramid [J]. Optical Engineering,1989,28(7):789-792
[61]Toet A. Multiscale contrast enhancement with applications to image fusion [J]. Optical Engineering, 1992,31(5):1026-1031
[62]Toet A. A morphological pyramidal image decomposition [J]. Pattern Recognition Letters,1989,9(4): 255-261
[63]G. K. Matsopoulos, S. Marshall, J. N. H. Brunt. Multiresolution morphological fusion of MR and CT images of the human brain[C]. Proceedings, of IEE, Vision, Image and Signal Processing,141(3), 1994.137-142
[64]Susanta Mukhopadhyay, Bhabatosh Chanda. Fusion of 2D grayscale images using multiscale morphology [J]. Pattern Recognition,2001(34):1939-1949
[65]Z. Liu, K. Tsukada, K. Hanaski et al. Image fusion by using steerable pyramid[J]. Pattern Recognition Letters,2001 (22):929-939
[66]Ishita De, Bhabatosh Chanda, Buddhajyoti Chattopadhyay. Enhancing effective depth-of-field by image fusion using mathematical morphology [J]. Image and Vision Computing,2006,24(12): 1278-1287
[67]Ishita De, Bhabatosh Chanda. A simple and efficient algorithm for multifocus image fusion using morphological wavelets[J]. Signal Processing,2006,86 (5):924-936
[68]Phen-Lan Lina, Po-Ying Huang. Fusion methods based on dynamic-segmented morphological wavelet or cut and paste for multifocus images[J]. Signal Processing,2008,88(6):1511-1527
[69]R. Redondo, F. Sroubek b, S. Fischer et al. Multifocus image fusion using the log-Gabor transform and aMultisize Windows technique[J]. Information Fusion,2009,10 (2):163-171
[70]Nikoukhah B R, Willsky A S. Multiscale system theory [A]. In:Proc 29th IEEE Conference on Decision and Control [C],1990.2484-2487
[71]Simone G, Morabito F C, Farina A. Radar image fusion by multiscale Kalman filtering [A]. In:Proc of the 3rd International Conference on Information Fusion[C],2,2000.10-17.
[72]J. M. Laferte, F. Heitz, P. Perez, E. Fabre. Hierarchical statistical models for the fusion of multiresolution image data[A], Fifth International Conference on Computer Vision (ICCV'95)[C], 1995:908-913
[73]Costantini M, Farina A, Zirilli F. The Fusion of Different Resolution SAR Images [A]. Proceedings of the IEEE[C],85(1),1997.139-146
[74]蒲恬,倪国强.应用于图像融合中的多尺度对比度调制法[J].北京理工大学学报,2000,20(2)：219-223
[75]李玲玲,周成平,丁明跃等.一种基于多尺度分解的多传感器图像融合方法[J].华中科技大学学报(自然科学版),2003,31(4)：82-84
[76]徐宝昌,陈哲.基于PCNN的多尺度对比度塔图像融合算法[J].计算机工程与应用,2004,21(10)：14-17
[77]张大明,阮祥伟,胡茂林.基于金字塔方法的SAR图像与光学图像的融合[J].雷达科学与技术,2005,3(6)：350-355
[78]朱瑞辉,范国滨,万敏.一种基于多分辨率分析的图像融合方法[J].光学与光电技术,2007,5(5)：82-84
[79]钱永浩,吴小俊,罗晓清.改进的基于形态学小波多聚焦图像融合方法[J].计算机工程与应用,2010,46(7)：158-161
[80]Chipman L J, Orr T M, Graham L N. Wavelets and image fusion [C]. Proc of Int Conf on Image Processing. Los Alamitos:IEEE Computer Society,1995.248-251
[81]Li H, Manjunath B S, Mitra S. Multisensor image fusion using the wavelet transform [J]. Graphical Models and Image Process,1995,57(3):235-245
[82]Hill P, Canagarajah N, Bull D. Image fusion using complex wavelets[C]. Proceedings of the British Machine Conference. Cardiff, UK,2002.487-496
[83]Youcef Chibani, Amrane Houacine. Redundant versus orthogonal wavelet decomposition for multisensor image fusion[J]. Pattern Recognition,2003,36 (4):879-887
[84]Gonzalo Pajares, Jesus Manuel de la Cruz. A wavelet-based image fusion tutorial[J]. Pattern Recognition,2004,37 (9):1855-1872
[85]Zhenhua Li, Zhongliang Jing, Xuhong Yang et al. Color transfer based remote sensing image fusion using non-separable wavelet frame transform[J]. Pattern Recognition Letters,2005,26 (13): 2006-2014
[86]Shutao Li, Bin Yang. Multifocus image fusion by combining curvelet and wavelet transform[J]. Pattern Recognition Letters,2008,29(9):1295-1301
[87]Chen Shao-hui, Su Hongbo, Zhang Renhua. Fusing remote sensing images using a trous wavelet transform and empirical mode decomposition[J]. Pattern Recognition Letters,2008,29(9):330-342
[88]Qiang Zhang, Bao-long Guo. Multifocus image fusion using the nonsubsampled contourlet transform[J]. Signal Processing,2009,89(7):1334-1346
[89]Shuyuan Yang, Min Wang, Licheng Jiao et al. Image fusion based on a new contourlet packet[J]. Information Fusion,2010(11):78-84
[90]Shutao Li, Bin Yang, Jianwen Hu. Performance comparison of different multi-resolution transforms for image fusion[J]. Information Fusion,2010:1-11
[91]王蓉,高立群,柴玉华等.一种多聚焦图像融合方法[J].控制与决策,2005,20(11)：1256-1260
[92]贾建,焦李成,孙强.基于非下采样Contourlet变换的多传感器图像融合[J].电子学报,2007,35(10)：1934-1938
[93]杨晓慧,金海燕,焦李成.基于DT-CWT的红外与可见光图像自适应融合[J].红外与毫米波学报,2007,26(6)：419-424
[94]杨风暴,倪国强,张雷.红外中波细分图像的小波包变换融合研究[J].红外与毫米波学报,2008,27(4)：275-279
[95]马艳军,徐立中,汤敏.遥感图像融合的非采样Contourlet变换方法[J].中国图象图形学报,2008,13(11)：2209-2216
[96]时海亮,方敏,梁锦锦.基于非亚采样Contourlet和SWT的多光谱图像和全色图像的融合算法[J].电子与信息学报,2009,31(1)：124-128
[97]金海燕,焦李成.基于CP和多小波HMT模型的克隆选择遥感图像融合[J].计算机学报,2009,32(7)：1434-1442
[98]刘斌,彭嘉雄.基于四通道不可分加性小波的多光谱图像融合[J].计算机学报,2009,32(2)：350-356
[99]龚建周,刘彦随,夏北成等.小波基及其参数对遥感影像融合图像质量的影响[J].地理与地理信息科学,2010,26(2)：6-10
[100]Gemma Piella. A general framework for multiresolution image fusion:from pixels to regions[J]. Information Fusion,2003,4 (4):259-280
[101]S. G. Nikolov, J. J. Lewis, R. J. O'Callaghan et al. Hybrid fused displays:Between pixel- and region-based image fusion[A]. Proceedings of the Seventh International Conference on Information Fusion[C],2,2004.1072-1079
[102]J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov. Pixel- and region-based image fusion with complex wavelets[J]. Information Fusion,2007,8(2):119-130
[103]Shutao Li, Bin Yang. Multifocus image fusion using region segmentation and spatial frequence [J]. Image and Vision Computing,2008,26(7):971-979
[104]Liu Kun, Guo Lei, Li Huihui et al. Fusion of Infared and Visible light images based on region segmentation[J]. Chinese Journal of Aeronautics,2009,22(1):75-80
[105]李晖晖,郭雷,刘航.基于区域分割的遥感图像融合方法[J].光子学报,2005,34(12)：1901-1905
[106]苏冬雪,吴小俊.基于区域颜色校正的遥感图像融合方法[J].江苏科技大学学报(自然科学版),2006,20(2)：60-64
[107]苏冬雪,吴小俊.基于多特征模糊聚类的图像融合方法[J].计算机辅助设计与图形学学报,2006,18(6)：838-843
[108]冯舒,蒋宏,任章.基于多分辨率分割的区域图像融合[J].计算机仿真,2007,24(5)：183-185
[109]岳晋,宦若虹,上官晋太.一种新的ICA域图像融合算法[J].光电工程,2008,35(5)：129-134
[110]叶传奇,王宝树,苗启广.一种基于区域特性的红外与可见光图像融合算法[J].光子学报,2009,38(6)：1498-1502
[111]Borotschnig H., Pinz A.. A new concept for active fusion in image understanding applying fuzzy set theory[C]. In Proceedings 5th IEEE International Conference on Fuzzy Systems 1996 (FUZZ-IEEE '96), New Orleans, volume 2,1996.793-799
[112]Prantl M., Ganster H., Pinz A.. Active fusion using bayesian networks applied to multi-temporal remote sensing imagery[C]. In Proceedings of the 13th ICPR, volume Ⅲ，1996.890-894
[113]Pinz A., Prantl M., Ganster H. et al. Active fusion-A new method applied to remote sensing image interpretation[J]. Pattern Recognition Letters,1996,17 (13):1349-1359
[114]M.-P. Dubuisson-Jolly, A. Gupta. Color and texture fusion:application to aerial image segmentation and GIS updating[J]. Image and Vision Computing,2000,18:823-832
[115]D.A.,Deng, H.. Fusion of Gabor filter and co-occurrence probability features for texture recognition[C]. IEEE Transactions on Image Processing, Vol.14, No.7,2005.925-936
[116]Yan Wu, Ming Li, Guisheng Liao. Multiple features data fusion method in color texture analysis[J]. Applied Mathematics and Computation,2007,185(2):784-797
[117]张易凡,何明一.综合有向纹理特征及其在多光谱图像融合中的应用[J].电子与信息学报,2007,29(1)：81-86
[118]王志慧,赵保军,冯永霖.基于特征提取和脉冲耦合神经网络的图像融合算法[J].光学技术,2009,35(4)：510-516
[119]P.Bogler. Shafer-Dempster reasoning with applications to multisensor target identification [J]:IEEE Trans, on System, Man, and Cybernetics,1987,6 (12):968-977
[120]Sateesha G.,Nadabar,Anil K. Jain.Fusion of range and intensity images on a connection machine(CM-2)[J]. Pattern Recognition,1995,28(1):11-26
[121]Fay D A, Waxman A M, Verly J G, et al. Fusion of visible, infrared and 3D LADAR imagery [A]. In: Proc of the 4th International Conference on Information Fusion[C], Montreal, SPIE-4363,2001. 219-230
[122]Sansanee Auephanwiriyakul, James M. Keller, Paul D. Gader. Generalized Choquet fuzzy integral fusion[J]. Information Fusion,2002,3(1):69-85
[123]Min Li, Wei Cai, Zheng Tan. A region-based multi-sensor image fusion scheme using pulse-coupled neural network[J]. Pattern Recognition Letters,2006,27(16):1948-1956
[124]Wei Huang, Zhongliang Jing. Multi-focus image fusion using pulse coupled neural network[J]. Pattern Recognition Letters,2007,28(9):1123-1132
[125]Zhaobin Wang, Yide Ma. Medical image fusion using m-PCNN[J]. Information Fusion,2008,9 (2):176-185
[126]Alaeddin Malek, MaryamYashtini. Image fusion algorithms for color and gray level images based on LCLS method and novel artificial neural network[J]. Neurocomputing,2010,73 (4):937-943
[127]杨烜,裴继红,杨万海.基于模糊积分的融合图像评价方法[J].计算机学报,2001,24(8)：815-818
[128]刘国良,强文义.基于粗神经网络的图像融合算法的研究[J].模式识别与人工智能,2003,16(3)：370-373
[129]鲍复民,李爱国,覃征.基于SGNN的图像融合[J].计算机研究与发展,2005,42(3)：417-423
[130]苗启广,王宝树.一种自适应PCNN多聚焦图像融合新方法[J].电子与信息学报, 2006,28(3)：466-470
[131]苗启广,王宝树.基于局部对比度的自适应PCNN图像融合[J].计算机学报,2008,31(5)：875-880
[132]Paul Scheunders. Multispectral image fusion using local mapping techniques[A]. in:Proceedings ICPR2000[C], Barcelona, Spain, vol.2,2000.2311-2315
[133]Farid Melgani, Sebastiano B. Serpico. A statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing images[J]. Pattern Recognition Letters,2002,23 (1):1053-1061
[134]Eloi Bosse, Pierre Valin et al. Exploitation of a priori knowledge for information fusion[J]. Information Fusion,2006,7(2):161-175
[135]R. Redondo, S. Fischer, F. S roubek. A 2D Wigner Distribution-based multisize windows technique for image fusion[J]. J. Visual Commun. Image Represent,2008,19(1):12-19
[136]陈洪波,王强,张孝飞等.基于小波系数邻域特征的图像融合[J].光学精密工程.2003,11(5)：516-522
[137]郭若杉,陈涛,彭思龙.基于局部线模型的彩色IC图像融合[J].中国图象图形学报.2005,10(4)：450-456
[138]龚昌来.一种改进的基于局部能量图像融合算法[J].光电工程.2008,35(11)：106-110
[139]曾立庆,蒋年德.基于邻域内相关系数与平均梯度的图像融合方法[J].计算机工程与设计.2010,31(7)：1533-1539
[140]Yufeng Zheng, Edward A Essock, Bruce C Hansen, Andrew M Haun. A new metric based on extended spatial frequency and its application to DWT based fusion algorithms [J]. Information Fusion,2007,8(2):177-192
[141]Wei Huang, Zhong Liangjing. Evaluation of focus measures in multi-focus image fusion [J]. Pattern Recognition Letters,2007,28(4):493-500
[142]Qu G, Zhang D, Yan P. Information measure for performance of image fusion [J]. Electronics Letters,2002,38(7):313-315
[143]Wang Z, Bovik AC. A universal image quality index [J]. IEEE Signal Processing Letters,2002,9(3): 81-84
[144]G Piella, H Heijmans. A new quality metric for image fusion [A], in Proceedings of IEEE:Image Processing [C]. Barcelona, Spain:IEEE Press,2003.173-176
[145]Wenzhong Shi, ChangQing Zhu, Yan Tian et al. Wavelet-based image fusion and quality assessment [J]. International Journal of Applied Earth Observation and Geoinformation,2005,6: 241-251
[146]Yufeng Zheng a, Edward A. Essock, Bruce C. Hansen et al. A new metric based on extended spatial frequency and its application to DWT based fusion algorithms [J]. Information Fusion,2007, 8(2):177-192
[147]Cui Yang, Jian-Qi Zhang, Xiao-Rui Wang et al. A novel similarity based quality metric for image fusion[J]. Information Fusion,2008,9(2):156-160
[148]Yin Chen, Zhiyun Xue, Rick S. Blum. Theoretical analysis of an information-based quality measure for image fusion[J]. Information Fusion,2008,9(2):161-175
[149]Yin Chen, Rick S. Blum. A new automated quality assessment algorithm for image fusion[J]. Image and Vision Computing,2009,27:1421-1432
[150]Chuanming Wei, Rick S. Blum. Theoretical analysis of correlation-based quality measures for weighted averaging image fusion[J]. Information Fusion,2010,11 (2):301-310
[151]Xiao-Qing Luo, Xiao-Jun WU.A New Metric of Image Fusion Based on Region SimilarityfJ]. Optical Engineering,2010,49(4):047006-1-047006-13
[152]胡良梅,高隽,何柯峰.图像融合质量评价方法的研究[J].电子学报,2004,32(12)：218-221
[153]王超,叶中付.基于相似性的图像融合质量的客观评估方法[J].软件学报,2006,7(7)：1580-15587
[154]何贵青,陈世浩,田沄等.多传感器图像融合效果综合评价研究[J].计算机学报,2008,31(3)：486-492
[155]Rick S.Blum, and Zheng Liu. Multi-Sensor Image Fusion and Its Applications[M],Taylor & Francis, Florida,USA,2006
[156]王润生.信息融合[M],科学出版社,2007年9月
[157]邓聚龙.灰色系统基本方法[M].武汉：华中理工大学出版社,1996
[158]Sun J, Xu W B. A global search strategy of quantum-behaved particle swarm optimization[C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems Singapore,2004.111-116
[159]S.N. Omkar, Rahul Khandelwal, T.V.S. Ananth et al. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures[J]. Expert Systems with Applications,2009,36:11312-11322
[160]徐锬,王超.基于模糊数学的数字图像模糊度[J].计算机辅助设计与图形学学报,2002,14(8)：747-749
[161]苏冬雪.图像融合理论及其应用的研究[D].[硕士学位论文],江苏科技大学,2005
[162]敬忠良,肖刚,李振华.图像融合--理论与应用[M].高等教育出版社,2007.
[163]H.j.Heijimans, j.Goutsias, Multi-resolution signal decomposition schemes. Part 2:Morphological wavelets[C], IEEE Transaction on Image processing,9,2000.1897-1913
[164]佘二永,王润生,徐学文.基于统计模型的遥感图像多分辨率融合方法[J],电子学报,2008,36(3)：490-493
[165]W. Cao, B. Li, and Y. Zhang, A remote sensing image fusion method based on PCA transform and wavelet packet transform[A]. In:Proc. IEEE Int. Conf. Neural Networks & Signal Processing[C], Nanjing, China, vol.14, Dec.2003.976-981
[166]邓自立Kalman滤波与维纳滤波——现代时间序列分析方法[M],哈尔滨：哈尔滨工业大学出版社,2001年7月
[167]Yixin Chen, James Z. Wang. A region-based fuzzy feature matching approach to content-based image retrieval[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(9): 1252-1267
[168]吴体瑞,吴小俊.一种基于区域和Contourlet变换的多聚焦图像融合新方法[J].中国体视学与图像分析,2007,12(2)：98-104
[169]章毓晋.中国图像工程及当前的几个研究热点[J].计算机辅助设计与图形学学报,2002,14(6)：489-500
[170]陆丽珍.基于多通道Gabor纹理特征的遥感图像检索[J].浙江大学学报(理学版),2004,31(6)：708-711
[171]焦峰.基于局部特征分析的人脸识别方法[J].计算机辅助设计与图形学学报,2003,15(1)：53-58
[172]朱勇,谭铁牛,王蕴红.基于笔迹的身份鉴别[J].自动化学报,2001,27(2)：229-234
[173]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别[J].自动化学报,2002,28(1)：1-10
[174]Xiao-Jun WU, Dong-Xue Su, Xiao-Qing Luo. A New Similarity Function for Region Based Image Fusion Incorporating with Gabor Filters and Fuzzy c-means clustering[C], SPIE,vol.6625,2008. 66250Z.1-66250Z.10
[175]Xiaojun Wu, Dongxue Su,Xiaoqing Luo. Multi-feature method for region based image fusion[C], Proceedings of Asigraph, Toyko, Japan, Digital Content Association of Japan(DCAJ),Dec.2008
[176]Jain A K, Prabhakar S, Hong L, et al. Filterbank-based fingerprint matching[J]. IEEE Transactions on Image Processing,2000,9(5):846-859
[177]覃征,等.数字图像融合[M].西安交通大学出版社,2004：92-100
[178]金剑秋,等.多光谱图象的真实感融合[J].中国图象图形学报,2002,7A(9)：926-931
[179]Sun J, Feng B, Xu W B.Particle Swarm Optimization with Particles Having Quantum Behavior[C], Proceedings of 2004 Congress on Evolutionary Computation,2004.325-331
[180]Hao Wang, Shiqin Yang, Jun Sun et al. Scalability of hybrid fuzzy c-means algorithm based on quantum-behaved PSO[C], Fourth international Conference on Fuzzy Systems and Knowledge Discovery, FSKD (2) 2007.261-265
[181]R. Hong and F. Sun, Structure similarity based objective metric for pixel-level image fusion[C],in Proc. The 2nd International Symposium on Systems and Control in Aerospace and Astronautics, Shenzhen, China,2008.1-6
[182]Liu K, Cheng Y Q, Yang J Y, et al. Algebraic feature extraction for image recognition based on an optimal discriminant criterion[J]. Pattern Recognition,1993,26 (6):903-911
[183]J Yang, D Zhang, J Y Yang, et al.. Two-dimensional PC A:A New Approach to Appearance-based Face Representation and Recognition [J]. IEEE Transactions on PAMI,2004,26(1):131-137
[184]Cheng H D, Chen J R, Li J. Threshold selection based on fuzzy c-partition entropy approach[J]. Pattern Recognition,1998,31(7):857-870
[185]刘贵喜,杨万海.基于小波分解的图像融合方法及性能评价[J].自动化学报,2002,28(6)：927-934
[186]C.S. Xydeas and V. Petrovic. Objective Image Fusion Performance Measure [J].Electronics Letters, 2000,36(4):306-309
[187]Shutao Li, Bin Yang. Multifocus image fusion using region segmentation and spatial frequency [J]. Image and Vision Computing,2008,26 (7):971-979
[188]F. Jahard, D.A. Fish, A.A. Rio and C.P. Thompson. Far/near infrared adapted pyramid-based fusion for automotive night vision[C], in Proc. IEEE 6th Int. Conf. on Image Processing and its Applications (IPA97),1997.886-890