红外图像视觉效果增强技术的研究
|
摘要
红外热成像技术在军事和民用等很多领域发挥着越来越重要的作用,它拓展了人类视觉认知极限,将人类的视觉感知范围由传统的可见光谱扩展到视觉不可见的红外辐射光谱区。但是,红外图像的对比度不高,视觉效果模糊,不利于进行观察和提取景物特征信息。对红外图像进行增强处理,提高红外图像的对比度,改善图像视觉效果,成为目前红外热成像领域研究的一个重要方面。
研究、分析和总结了图像增强方法,并针对目前图像增强算法中普遍存在的图像局部细节保护和噪声滤除之间的矛盾,提出了基于单幅图像多尺度方向分析的红外图像增强方法和基于多源图像融合的图像增强方法。
多尺度方向分析是近年来在小波分析的基础上发展起来的图像稀疏表示方法。论文将多尺度方向分析理论应用于红外图像的去噪增强处理中,提出一种基于NSCT (Nonsubsampled Contourlet)变换的红外图像增强方法。利用NSCT变换在处理图像几何结构方面的优势,对红外图像的噪声和边缘信息分别进行处理,从而能够在增强图像边缘和细节信息的同时抑制图像噪声。NSCT变换不但继承了Contourlet变换的多尺度、多方向性,同时还具备了平移不变特性,因此能够更好地保持图像边缘。
近年来,多源图像的使用导致了信息表现形式的多样性,将多源图像合成到一幅图像中显示,不仅适合图像进一步的处理和分析,而且更满足了人类视觉感受的需要。图像融合技术可以将在同一时间、或不同时间获取的关于某个具体场景的多源图像信息加以综合,并生成一个新的有关此场景的描述。红外和可见光成像传感器是两种功能原理不同的图像传感器。红外成像传感器检测目标发出的不可见热辐射,而可见光成像传感器则通过吸收目标反射的可见光波段电磁波来实现对目标的探测。通过将可见光图像和红外图像这两种不同类型图像的融合,得到了单幅图像增强技术所达不到的视觉增强效果。
面向视觉增强的图像融合技术主要问题在于如何将多源图像中的视觉信息有效的合成到一幅图像中去显示,并同时抑制源图像中的噪声,以增强融合图像的视觉效果。将多尺度方向分析方法与多源图像融合技术结合起来,提出了基于统计特性的图像融合增强方法和基于区域特性的图像融合增强方法。
考虑到传感器噪声的存在,设计了基于统计模型的多源图像融合增强方法,不仅更有效地融合了多源图像的信息,而且抑制了传感器噪声对图像融合的影响。
人类视觉系统对图像清晰度的判断是由区域内像素共同体现的。根据NSCT变换后的低频子带和高频方向子带的特性,设计了基于区域特性的图像融合方法,在增强融合图像视觉效果的同时,有效地保持融合图像的细节和纹理信息。
经过红外与可见光图像的融合实验表明,面向视觉增强的图像融合技术能够明显改善单一传感器的不足,提高图像信息的利用效率,从而更为准确和全面地获取对目标或场景的信息描述。
基于单幅图像多尺度方向分析的红外图像增强方法和基于多源图像融合的图像增强方法各有自己的特点。前者是针对单一图像采用多尺度方向分解的方法进行的变换域图像处理,以去除图像中的噪声并增强细节和边缘等有用信息;而后者是将多个图像传感器采集到的图像进行融合以得到比单一图像传感器更丰富的图像信息。这两类图像增强方法适用于不同的图像处理环境,均能取得较好的增强效果。
The technology of infrared thermal imaging plays a more and more important role in the military, civil field and other fields. It has enlarged the range of the human’s visual cognition from the visible spectrum zone to the invisible infrared spectrum region. However, the value of the contrast ratio of the infrared image is low, and the visual effects of it are not good. Therefore, the infrared image goes against observation and extraction of scenery’s feature. The enhancement of the infrared image for improving its contrast ratio and visual effects has become one important aspect of the current thermal imaging field.
The dissertation has studied on the measures of image enhancement and summarized the current approaches. The dissertation has put forward the measure of image enhancement based on the multi-scale directional analysis of the individual image and the fusion of multi-source image. The measure aims to solve the contradiction between the protection of the image’s partial detail and the noise filtering.
The multi-scale directional analysis is the sparse representation method based on wavelet analysis which has been developed in recent years. The dissertation has applied the multi-scale directional analysis to the de-noise and enhancement treatment of infrared image, and put forward the infrared image enhancement measure which is based on Nonsubsampled Contourlet transform. Taking the advantage of the Nonsubsampled Contourlet transform in the treatment of image’s geometrical structure, the infrared image’s noise and marginal information can be dealt with separately, therefore, the de-noise can be realized while the image’s marginal and detail information has been reserved. Nonsubsampled Contourlet transform has not only inherited the benefits of the multi-scale, multi-directional of the Contourlet transform, but also has the feature of translation invariant. It can reserve the image’s marginal information more effectively.
In recent years, the application of multi-source image has resulted in the diversity of the image information manifestation. The integration of the multi-source image into one image can meet the requirement of human’s visual cognition and is beneficial to the further analysis by the computer. The image fusion technology can integrate the multi-source image about certain concert scenery acquired at the same time or at different time into a new description about the scenery. Infrared and visible light imaging sensor are two different kinds of image sensors with different function and principle. The infrared imaging sensor can detect the invisible thermal radiation, while the visible light sensor can detect the target through the absorbing of the visible light band electromagnetic wave reflected by the target. Through the fusion of the visible light image and the infrared image, we can achieve much better visual effects than those of the individual image enhancement technology.
The problem of the image fusion technology merely working in the visual enhancement rests with how to effectively synthesis the visual information in the multi-source image into one image and how to de-noise the source image to enhance the fused image’s visual effects. The dissertation combines the multi-scale directional analysis and the multi-source image fusion approaches, and brings forward the image fusion enhancement measures based on the statistical characteristics and the regional features respectively.
Considering the existence of sensor’s noises, the multi-source image fusion enhancement measure which is based on the statistical model has been put forward. The measure not only more effectively fuses the information of the multi-source image, but also restrains the influence of sensor’s noises to the fused image.
The judgment of the human’s visual system towards the image definition is commonly embodied by the pixels in the region. According to the characteristic of low frequency sub-band and high frequency directional sub-band after the NSCT transform, we designed the image fusion measures based on regional features, which enables the effective reservation of the marginal and detail information while enhancing the fusion image visual effects.
According to the infrared and visible image fusion experiments, the image fusion technology aiming at improving the image visual effects can make up for the deficiency of single sensor, improve the use efficiency of image information, therefore, provide a more accurate and comprehensive description towards the target or the scene.
The infrared image enhancement measure based on the individual image multi-scale directional analysis and the image enhancement measure based on the multi-source image fusion have their own advantages and disadvantages. The former is aimed at dealing with individual image which has adopted the multi-scale directional analysis in carrying out the frequency domain treatment to de-noise and enhance the marginal and detail information; the later is to fuse the image collected by the multi-source image sensors to acquire the image information which obtains more information than the individual image sensor. The two image enhancement measures apply in different image treatment environment, and both can acquire better effects of enhancement.
研究、分析和总结了图像增强方法,并针对目前图像增强算法中普遍存在的图像局部细节保护和噪声滤除之间的矛盾,提出了基于单幅图像多尺度方向分析的红外图像增强方法和基于多源图像融合的图像增强方法。
多尺度方向分析是近年来在小波分析的基础上发展起来的图像稀疏表示方法。论文将多尺度方向分析理论应用于红外图像的去噪增强处理中,提出一种基于NSCT (Nonsubsampled Contourlet)变换的红外图像增强方法。利用NSCT变换在处理图像几何结构方面的优势,对红外图像的噪声和边缘信息分别进行处理,从而能够在增强图像边缘和细节信息的同时抑制图像噪声。NSCT变换不但继承了Contourlet变换的多尺度、多方向性,同时还具备了平移不变特性,因此能够更好地保持图像边缘。
近年来,多源图像的使用导致了信息表现形式的多样性,将多源图像合成到一幅图像中显示,不仅适合图像进一步的处理和分析,而且更满足了人类视觉感受的需要。图像融合技术可以将在同一时间、或不同时间获取的关于某个具体场景的多源图像信息加以综合,并生成一个新的有关此场景的描述。红外和可见光成像传感器是两种功能原理不同的图像传感器。红外成像传感器检测目标发出的不可见热辐射,而可见光成像传感器则通过吸收目标反射的可见光波段电磁波来实现对目标的探测。通过将可见光图像和红外图像这两种不同类型图像的融合,得到了单幅图像增强技术所达不到的视觉增强效果。
面向视觉增强的图像融合技术主要问题在于如何将多源图像中的视觉信息有效的合成到一幅图像中去显示,并同时抑制源图像中的噪声,以增强融合图像的视觉效果。将多尺度方向分析方法与多源图像融合技术结合起来,提出了基于统计特性的图像融合增强方法和基于区域特性的图像融合增强方法。
考虑到传感器噪声的存在,设计了基于统计模型的多源图像融合增强方法,不仅更有效地融合了多源图像的信息,而且抑制了传感器噪声对图像融合的影响。
人类视觉系统对图像清晰度的判断是由区域内像素共同体现的。根据NSCT变换后的低频子带和高频方向子带的特性,设计了基于区域特性的图像融合方法,在增强融合图像视觉效果的同时,有效地保持融合图像的细节和纹理信息。
经过红外与可见光图像的融合实验表明,面向视觉增强的图像融合技术能够明显改善单一传感器的不足,提高图像信息的利用效率,从而更为准确和全面地获取对目标或场景的信息描述。
基于单幅图像多尺度方向分析的红外图像增强方法和基于多源图像融合的图像增强方法各有自己的特点。前者是针对单一图像采用多尺度方向分解的方法进行的变换域图像处理,以去除图像中的噪声并增强细节和边缘等有用信息;而后者是将多个图像传感器采集到的图像进行融合以得到比单一图像传感器更丰富的图像信息。这两类图像增强方法适用于不同的图像处理环境,均能取得较好的增强效果。
The technology of infrared thermal imaging plays a more and more important role in the military, civil field and other fields. It has enlarged the range of the human’s visual cognition from the visible spectrum zone to the invisible infrared spectrum region. However, the value of the contrast ratio of the infrared image is low, and the visual effects of it are not good. Therefore, the infrared image goes against observation and extraction of scenery’s feature. The enhancement of the infrared image for improving its contrast ratio and visual effects has become one important aspect of the current thermal imaging field.
The dissertation has studied on the measures of image enhancement and summarized the current approaches. The dissertation has put forward the measure of image enhancement based on the multi-scale directional analysis of the individual image and the fusion of multi-source image. The measure aims to solve the contradiction between the protection of the image’s partial detail and the noise filtering.
The multi-scale directional analysis is the sparse representation method based on wavelet analysis which has been developed in recent years. The dissertation has applied the multi-scale directional analysis to the de-noise and enhancement treatment of infrared image, and put forward the infrared image enhancement measure which is based on Nonsubsampled Contourlet transform. Taking the advantage of the Nonsubsampled Contourlet transform in the treatment of image’s geometrical structure, the infrared image’s noise and marginal information can be dealt with separately, therefore, the de-noise can be realized while the image’s marginal and detail information has been reserved. Nonsubsampled Contourlet transform has not only inherited the benefits of the multi-scale, multi-directional of the Contourlet transform, but also has the feature of translation invariant. It can reserve the image’s marginal information more effectively.
In recent years, the application of multi-source image has resulted in the diversity of the image information manifestation. The integration of the multi-source image into one image can meet the requirement of human’s visual cognition and is beneficial to the further analysis by the computer. The image fusion technology can integrate the multi-source image about certain concert scenery acquired at the same time or at different time into a new description about the scenery. Infrared and visible light imaging sensor are two different kinds of image sensors with different function and principle. The infrared imaging sensor can detect the invisible thermal radiation, while the visible light sensor can detect the target through the absorbing of the visible light band electromagnetic wave reflected by the target. Through the fusion of the visible light image and the infrared image, we can achieve much better visual effects than those of the individual image enhancement technology.
The problem of the image fusion technology merely working in the visual enhancement rests with how to effectively synthesis the visual information in the multi-source image into one image and how to de-noise the source image to enhance the fused image’s visual effects. The dissertation combines the multi-scale directional analysis and the multi-source image fusion approaches, and brings forward the image fusion enhancement measures based on the statistical characteristics and the regional features respectively.
Considering the existence of sensor’s noises, the multi-source image fusion enhancement measure which is based on the statistical model has been put forward. The measure not only more effectively fuses the information of the multi-source image, but also restrains the influence of sensor’s noises to the fused image.
The judgment of the human’s visual system towards the image definition is commonly embodied by the pixels in the region. According to the characteristic of low frequency sub-band and high frequency directional sub-band after the NSCT transform, we designed the image fusion measures based on regional features, which enables the effective reservation of the marginal and detail information while enhancing the fusion image visual effects.
According to the infrared and visible image fusion experiments, the image fusion technology aiming at improving the image visual effects can make up for the deficiency of single sensor, improve the use efficiency of image information, therefore, provide a more accurate and comprehensive description towards the target or the scene.
The infrared image enhancement measure based on the individual image multi-scale directional analysis and the image enhancement measure based on the multi-source image fusion have their own advantages and disadvantages. The former is aimed at dealing with individual image which has adopted the multi-scale directional analysis in carrying out the frequency domain treatment to de-noise and enhance the marginal and detail information; the later is to fuse the image collected by the multi-source image sensors to acquire the image information which obtains more information than the individual image sensor. The two image enhancement measures apply in different image treatment environment, and both can acquire better effects of enhancement.
引文
[1]唐浩,刘学智,吴云峰等.有源低热器件红外热图像增强新方法[J].光电工程. 2006, 33(2):98-101.
[2] Keenan E, Wright R.G., Zgol M. Infrared laser imaging for circuit board and IC failure detection[C].AUTOTESTCON (Proceedings), Future Sustainment for Military and Aerospace, 2003:399-406.
[3]周鹏,王明时,陈书旺等.基于红外图像处理的埋地石油管道自动探测技术[J].天津大学学报. 2007, 40(1):88-93.
[4]刘建都,张善文,李续武.基于脊波变换的一种红外图像增强技术[J].宇航计测技术. 2007, 27(2):62-64.
[5] E.F.J. Ring. The historical development of temperature measurement in medicine[J]. Infrared Physics & Technology. 2007,49(3):297-301.
[6]胡斌.医用红外热图处理及辅助诊断系统的设计[D].天津:天津大学.2006.
[7] W.K.Prat.Digital Image Porcessing,2nd Ed.John Wiley,New York,1991.
[8]章毓晋.图像工程(上册)-图像处理和分析[M].北京:清华大学出版社,1999.
[9]高鑫.基于小波变换和PDE模型噪声与模糊图像恢复[D].北京:北京师范大学.2001.
[10]王超.基于变分问题和偏微分方程的图像处理技术研究[D].合肥:中国科技大学.2007.
[11]薛琴.基于多尺度分析的图像增强研究[D].南昌:江西师范大学.2006.
[12]王胜军.图像增强方法的研究[D].大连:大连海事大学.2005.
[13] Y.S.Choi and R.Krishnapuram,A Robust Approach to Image Enhancement Based on Fuzzy Logic[J],IEEE Transaction on Image Processing,6(6),1997.
[14]容观澳.计算机图像处理[M].北京:清华大学出版社,2000.
[15]张悦.基于模糊逻辑和小波理论的图像增强算法研究[D].保定:华北电力大学, 2007.
[16]张红慧.基于多尺度小波域相关性的图像去噪与增强方法的研究[D].西安:西北大学,2007.
[17] McCauley H. Image enhancement of infrared uncooled focal plane array imagery[J].Proceedings of the SPIE - The International Society for Optical Engineering,Infrared Systems and Components III.1050:28-34,1989.
[18] Chun Moo Lo.Forward looking infrared (FLIR) image enhancement for the automatic target cuer system. Proceedings of the Society of Photo-Optical Instrumentation Engineers[J], Image Processing for Missile Guidance. 1980 , 238:91-102.
[19] Silverman, Jerry. Signal-processing algorithms for display and enhancement of IR images[J].Proc. SPIE-- The International Society for Optical Engineering. 1993,2020:440-450.
[20] Virgil M,Vickers E. Plateau equalization algorithm for rea1-time display of high-quality infrared imagery[J]. Optical Engineering. 35(7): 1921-1926, 1996.
[21] John B Zimmerman. An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement[J]. Transactions on Medical Imaging. 1988, 7(4):304-312.
[22] Jadwiga Rogowska,Kendall Preston,and Donald Sashin. Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs[J]. IEEE Trans on Biomedical Engineering. 1988, 35(10):817-827.
[23]陈文建,迟泽英.红外烟幕遮蔽条件下目标热像的增强[J].红外技术. 2003,25(4):66-69.
[24]邸慧,于起峰,张小虎.一种基于灰度变换的红外图像增强算法[J].应用光学.2006,1(6):12-14.
[25]殷德奎.张保民.柏连发.红外图像的二维灰度变换增强方法[J].红外技术.1999,21(3):25-29.
[26] Kamel Belkace-Boussaid and Azeddine Beghdadi,A New Image Smoothing Method Based on a Simple Model of Spatial Processing in the Early Stages of Human Vision[J], IEEE Transactions on Image Processing, 9(2): 220-226,2000.
[27] Yajun Fang, Keiichi Yamada,Yoshiki Ninomiya, Berthold K.P.Horn, Ichiro Masaki, A Shape-Independent Method for Pedestrian Detection With Far-infrared Images[J], IEEE Transations on Vehicular Technology. 2004, 53(6): 1679-1697.
[28] N. Scales, C. Herry, M. Frize. Automated Image Segmentation for Breast Analysis Using Infrared Images[C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS. 2004,1737-1740.
[29]向健勇,徐军.一种实用的红外图像分割算法研究[J].西安电子科技大学学报, 1997,24(3):416-420.
[30]林晓春,王艳.一种基于图像融合的红外图像增强的新方法[J].红外技术. 2004,26(2):48-53.
[31]王磊.红外图像增强算法研究及其实时实现技术[D].西安:西安电子科技大学.2007.
[32] Rafael C.Gonzalez,Richard E.Woods, Digital Image Processing, Second Edition , Publishing House of Electronics Industry. 2002,148-215.
[33] Jinshan Tang and Scot Acton,Image Enhancement Using a Contrast Measure in the Compressed Domain[J],IEEE Signal Processing Leters,Oct 2003, 10(10):289-292.
[34] Stephane Mallat, Sifen Zhong. Characterization of Signal from Multiscale Edges[J],IEEE Transaction on Patenr Analysis and Machine In telligence, 1992, 14(7):710-732.
[35]王耀南,李树涛,毛建旭.计算机图像处理与识别技术[M],北京:高等教育出版社,2001,91-96.
[36] Tamar Peli, Jee S. Lim. Adaptive filtering for image enhancement[J], Optical Engineering,1982,21(1):108-112.
[37] H.Pege McAdams, G. Allan Johnson, S.A. Suddarth, C.E.Ravin. Implementation of adaptive filtration for digital chest imaging[J], Optical Engineering, 1987, 26(7): 669-673.
[38] Pabio G. Tahoces, Jose Correa, Miguel Sauto, Carmen Gonzalez. Enhancement of chest and breast radiographs by automatics patial filtering[J], IEEE Trans on Medical Imaging, 1991, 10(1):330-335.
[39] Haiguang Chen, Andrew Li, Leon Kaufman,and James Hale. A fast filtering algorithm for image enhancement[J], IEEE Trans on Medical Imaging, 1997, 13(3): 557-564.
[40]宫武鹏,王永仲.一种基于小波变换的红外图像对比度增强技术[J].国防科技大学学报.2000,22(6):117-119.
[41]张瑾.基于小波变换的红外图像处理应用研究[D].成都:西南交通大学.2006.
[42] Raghuveer M.R. Image enhancement and denoising by wavelet transform for concealed weapon detection[J]. Proceedings of the SPIE - The International Society for Optical Engineering. 1997, 2942: 112-122.
[43] Sadjadi Firooz A. Infrared target detection with probability density functions of wavelet transform subbands[J].Applied Optics. 2004,43(2):315-323.
[44] Richard Alan Peters, A New Algorithm for Image Noise Reduction using Mathematical Morphology[J], IEEE Trans. on Image Processing, 1995,4(3):554-568.
[45] J G M Schavemaker. Image Sharpening by Morphological Filtering[J]. Pattern Recognition. 2000, 33:997-1012.
[46]冯国进,顾国华,陈钱.基于形态学的红外图像边缘增强[J].激光与红外.2003, 33(6):453-454.
[47] King I.,Xu L.Adaptive contrast enhancement by entropy maximization with a 1-K-1 constrained network[C].Proceedings of International Conference on Neural Information Processing (ICONIP '95), 1995, 2(2):703-706.
[48] F.Russo and G.Rampom,Edge Extraction by FIRE Operators[C],In Proc.Third IEEE Int.Conf.Fuzzy Systems, Orlando.FL.1994,pp.249-253.
[49] Kamel Belkacem-Boussaid and Azeddine Beghdadi,A New Image Smoothing Method Based on a simple model of Spatial Processing in the Early Stages of Human Vision[J], IEEE Trans. on Image Processing, 2000,9(2):220-226.
[50] Hollingworth G.,Tyrrell A. and Smith S.Simulation of Evolvable Hardware to Solve Low Level Image Processing Tasks[J].LNCS-Springer,Vol.1596:46-58,1999.
[51] Jain A.Fundamentals of Digital Image Processing,Prentice Hall,1991.
[52] Nakao Z.,Takashibu G.,Ali F.E. and Chen Y.W.,Evolutionary CT Image Reconstruction[C].Proceedings of ICNN'97,1997,1608-1611.
[53]过润秋,李俊峰,林晓春.基于并行遗传算法的红外图像增强及相关技术[J].西安电子科技大学学报(自然科学版).2004,31(1):6-8,20.
[54]朱菊华,杨新,李俊等.基于纹理分析的保细节平滑滤波器[J].中国图形图像学报. 2001,6(11):11-15.
[55] Tony F.chan,Stanley Osher,Jianhong Shen,The Digital TV Filter and Nonlinear Denoising[J]. IEEE Trans. on Image Proeessing. 2001,10(2): 231-241.
[56] Tony F.Chan,Luminita A.Vese, Active Contours Without Edges[J].IEEE Trans.on Image Processing. 2001,10(2): 266-277.
[57]王超.基于变分问题和偏微分方程的图像处理技术研究[D].合肥:中国科学技术大学.2007.
[58]林晓春,李存志.一种基于图像融合的红外图像增强新方法[J].西安电子科技大学学报.2005,32(2):189-192,215.
[59]吉书鹏,丁晓青.可见光与红外图像增强融合算法研究[J].红外与激光工程.2002,31(6):518-521,544.
[60] Yang J, Blum R S. Image Fusion Using the Expectation-maximization Algorithm and a Hidden Markov Models [C].IEEE Vehicular Technology Conference. Los Angeles: IEEE, 2004,6:4563-4567.
[61]毛士艺,赵巍.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.
[62]刘建都,张善文,李续武.基于脊波变换的一种红外图像增强技术[J].宇航计测技术.2007,27(2):62-64.
[63] E.J.Candes. Ridgelets: Theory and application[D]. Stanford University, 1998.
[64] J.L.Starck,E.Candes,and D.L.Donoho,The Curvelet Transform for Image Denoising[J]. IEEE Trans.Image Processing, 2002, 11:131-141.
[65] E.L.Pennec,S.Mallat. Sparse geometric image representation with bandelets[J]. IEEE Transcations on Image Processing, 2005,14(4):423-438.
[66] G.Peyre,S.Mallat. Discrete bandelets with geometric orthogonal filters[C]. IEEE International Conference on Image Processing, Genoa, Italy.2005:65-68.
[67] Romberg J.K., Wakin M., Baraniuk R., Multiscale wedgelet image analysis: fast decompositions and modeling[C]. Proceedings 2002 International Conference on Image Processing. 2002, vol.3:585-588.
[68] Wu Ru-Shan, Chen Ling. Directional illumination analysis using beamlet decomposition and propagation[J]. Geophysics. 2006,71(4):147-159.
[69] Minh N. Do., Martin Vetterli.The Contourlet Transform: An Efficient Directional Multiresolution Image Representation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2005, 14(12):2091-2106.
[70]李智杰.稀疏信号表示理论及其在图像增强中的应用[M].西安:西安电子科技大学.2005.
[71]李洪均,梅雪,林锦国.基于Contourlet域HMT模型的红外图像去噪算法[J].红外技术.2007,29(6):357-360.
[72] Belbachir A.N., Goebel P.M..A combined multiresolution approach for faint source extraction from infrared astronomical raw images sequence[J].2005 IEEE/SP 13th Workshop on Statistical Signal Processing (IEEE Cat. No. 05TH8839) .419-424 Vol.1,2006.
[73] Kamel Belkacem-Boussaid and Azeddine Beghdadi,A New Image Smoothing Method Based on a simple model of Spatial Processing in the Early Stages of Human Vision[J], IEEE Trans. on Image Processing,9(2):220-226,2000.
[74] Rafael C.Gonzalez,Richard E. Woods.数字图像处理(第二版)[M].北京:电子工业出版社. 2003.
[75]陈传波,金先级.数字图像处理[M].北京:机械工业出版社.2004.
[76]邵凡麒.红外图像处理[D].南京:南京理工大学,2005.
[77]李宏贵,李兴国,李国祯等.一种基于遗传算法的红外图像增强方法[J]. Systems Engineering and Electronics.1999,21(7):44-46.
[78]练秋生.基于视觉特性的多方向小波构造及其应用研究[D].秦皇岛:燕山大学.2006.
[79] Mallat, Stephane G. Theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1989,11(7):674-693.
[80]何锦平.基于小波多分辨分析的图像增强及其应用研究[D].西安:西北工业大学,2003.
[81]王博.数字图像处理方法与应用研究[D].西安:西北工业大学, 1998.
[82]张日华,黄彦明.小波变换及其在图像处理中的特性分析[J].中国图像图形学报.1997,2(7).
[83]侯波.基于小波变换消除遥感图像噪声[D].北京:中国科学院遥感应用研究所, 2002.
[84]王建勇.二维图像降噪、特征检测的算法及实用化技术研究[D].北京:北京邮电大学,2005.
[85]魏政刚等.图像质量评价方法的历史、现状和未来[J].中国图像图形学报.1998,3(5):236-239.
[86]殷晓丽,方向忠,翟广涛.一种JPEG图片的无参考图像质量评价方法[J].计算机工程与应用.2006,18(3):79-81,129.
[87] CCIR Rec. 500-5."Method for the subjective assessment of the quality of television pictures", Recommendations of the ITU, Telecommunications Standardization Sector, 1990.
[88]谢晋.基于Contourlet变换图像去噪算法研究[D].西安:西北工业大学,2007.
[89]焦李成,谭山.图像的多尺度方向分析:回顾与展望[J].电子学报. 2003, 31(12A) :1975-1981.
[90] F.G. Meyer, R.R.Coifman. Brushlets: a tool for directional image analysis and image compression[J]. Journal of Appl. and Comput. Harmonic Analysis, 1997, 5: 147-187.
[91] A.Cohen, B.Matei. Compact representation of images by edge adapted multiscale transforms IEEE Int. Conf Image Process., Thessaloniki, Greece, 2001:8-11.
[92] D.L.Donoho. Wedgelets: Nearly-minimax estimation of edges[J]. Ann. Statist, 1999, 27 :859-897.
[93] J.Romberg, M.Wakin,R. Baraniuk. Approximation and Compression of Piecewise Smooth Images Using a Wavelet/Wedgelet Geometric Model[C]. IEEE Intenrational Conference on Image Processing,Barcelona,Spain,2003:49-52.
[94] E.L.Pennec,S. Mallat.Sparse geometric image repersentation with bandelets[J]. IEEE Trans.Image Process.,2005,14(4):423-438.
[95] G. Peyre,S.Mallat.Discerte bandelets with geometric orthogonal filters[C]. IEEE Intenrational Confeernce on Image Processing, Genoa, Italy,2005:65-68.
[96] V.Velisavljevic, B.Beferull-Lozano, M.Vetterli, P.L.Dragotti. Approximation power of directionlets[C], IEEE Intenrational Conference on Image Processing, Genoa, Italy, 2005:741-744.
[97] E.J.Candes. Ridgelets: Theory and application[D]. Stanford University, 1998:20-55.
[98] E.J.Candes. Harmonic analysis of neural networks[J]. Applied and Computational Harmonic Analysis, 1999,6(2):197-218.
[99] D.L.Donoho. Orthonormal ridgelets and linear singularities. [Stanford University, Report], 1998: 1-15.
[100] M.N.Do, M.Vetterli, Orthonormal finite ridgelet transform for image compression[C], IEEE Intenrational Conference on Image compression, Vancouver, Canada, 2000:367-370.
[101]侯彪,刘芳,焦李成.基于脊波变换的直线特征检测[J].中国科学(E辑). 2003,33(1):65-73.
[102] E.J.Candes, D.L.Donoho. Curvelets-a surprisingly effective nonadaptive representation for objects with edges. In: Cohen A., RabutC., Schumaker L.L. editors. Curve and surface fitting. Saint-Malo: Vanderbilt University Press, 1999:105-120.
[103] J.L.Starck, E.J.Candes, D.L.Donoho. The curvelet transform for image denoising[J]. IEEE Transaction on Image Processing, 2002, 11(6): 670-684.
[104] E.J.Candes, F.Guo. New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction[J]. Signal Processing, 2002, 82(11):1519-1543.
[105] E.J.Candes, D L Donoho. Digital Curvelet transform via USFFT. [Stanford University, Report], 2004:1-35.
[106] M.N.Do, M. Veterli. Contourlets and sparse image expansions[C]. Proceedings of The Intenrational Society for Optical Engineering, San Diego, USA, 2003: 560-570.
[107] M.N.Do, M.Veterli. The contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Transaction on Image Porcessing, 2005,14(12):2091-2106.
[108] D.L.Donoho, M. Vetterli, R.A.DeVore. Data compression and harmonic analysis[J]. IEEE Trans. Information Theory, 1998,44(6):2435-2476.
[109] A. Skodras, C. Christopoulos, T. Ebrahimi.The JPEG 2000 still image compression standard[J]. IEEE Signal Processing Magazine, 2001,18:36–58.
[110]唐永茂.多尺度方向分析在图像处理中的应用研究[D].上海:上海交通大学,2005.
[111] Minh N. Do, Member, Martin Vetterli. The Contourlet Transform: An Efficient Directional Multiresolution Image Representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[112]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法[J].计算机应用研究, 2006,9(20): 18-22.
[113] Ramin Eslami, Hayder Radha. The Contourlet Transform for Image De-noising Using Cycle Spinning[C]. Conference Record of the Asilomar Conference on Signals, Systems and Computers. 2003: 1982-1986.
[114] Burt P.J., Adelson E.H. The Laplacian pyramid as a compact image code[J], IEEE Trans. Commun., 1983, 31(4): 532-554.
[115] R.H.Bambegrer, M.J.T.Smith. A filter bank for the directional decomposition of images: Theory and design[J]. IEEE Trans. Singal Proc. 1992, 40(4):882-893.
[116] P.Vaidyanathan. MultiMate Systems and Filter banks. Englewood Cliffs, NJ :Prentice-Hall,1993.
[117] R.Ansari.m. Efficient IIR and FIR fan filters[J]. IEEE Trans. Circuits Syst.,1987:941-945.
[118] R.H.Bamberger,M.J.T.Smith.A filter bank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Proc., 1992(40):882-893.
[119] M.N.Do.Directional Multiresolution Image Representations[D]. Department of Communication System. Swiss Federal Insittute of Technology Lausanne,2001.
[120]苗启广.多传感器图像融合方法研究.博士学位论文.西安电子科技大学,西安,2005.
[121] A Gersho, B.Ramaurthi. Image coding using vector quantization[C]. IEEE Int. Conf.Acoust, speed signal Processing. 1982,428-431.
[122] Stromberg J.O. A modfied Franklin system and higher order spline systems on R.n. as uncondition bases for Hardy spaces[J].Trans.Amer.Math.Soc.1982,475-494.
[123] Gabor D. Theory of communication[J]. Journal of Institute for Electrical Engineering, SPIE, 1946,93:429-457.
[124] Meyer Y. Wavelets and operators. Cambridge.UK,1992,3(2):45-64.
[125] Mallet S. Multi-resolution approximations and wavelet orthonormal bases of L2(R)[J]. Transaction of American Mathematical Society, 1989, 315(1): 69-87.
[126] Mallat S. A theory for multiresolution signal decomposition[J].IEEE Trans. on PAMI,1989,11(7):674-693.
[127] Daubechies I.Orthonormal bases of compactly supported wavelets[J]. Comm. Pure Appl.Math.,1988,41:909-996.
[128] C K Chui,J Z Wang.A cardinal spline approach to wavelets [J].Proc Amer Math Soc,1991,113:785-793.
[129] Coifman R R,Wickerhauser M W.Entropy-based algorithms for best basis selection [J].IEEE Transactions on Information Theory,1992,38(2):713-718.
[130] S.G.Chang, B.Yu, and M. Vetterli, Spatially adaptive wavelet thresholding with context modeling for image denoising[J]. IEEE Trans. Image Process., 2000, 9(9):1522-1531.
[131] S.G.Chang, B.Yu, and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Trans on Image Processing, 2000, 9(9): 1532-1546.
[132] R. R. Coifman, D. L. Donoho. Translation-invariant de-noising[C]. Wavelets and Statistics, Springer Lecture Notes in Statistics 103, New York: Springer,Verlag, 1995. 125-150.
[133] Alyson K. Fletcher, Kannan Ramchandran, Vivek K. Goyal. Wavelet denoising by recursive cycle spinning[C]. Proc. IEEE International Conference Image Processing, Rochester, NY: 2002. 873-876.
[134] Eslami R, Radha H. The contourlet transformfor image de-noising using cycle spinning[C]. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2003. 1982-1986.
[135] A.L.Cunha, J.Zhou, Minh.N.Do.The nonsubsampled contourlet:theory, design and applications[J]. IEEE Transactions on Image Processing.2006, 15(10):1779-1793.
[136]章鹏.图像的方向多分辨率分析[D].合肥:中国科学技术大学, 2006.
[137] Cunha Arthur L., Do Minh N.Filter design for directional multiresolution decomposition[J]. Proceedings of SPIE- The International Society for Optical Engineering, Wavelets XI, 2005, 5914:1-10.
[138] Da Cunha Arthur L.,Zhou, J., Do, Minh N.The nonsubsampled contourlet transform: Theory, design, and applications.IEEE Transactions on Image Processing. 2006,15(10):3089-3101.
[139] Jianping Zhou,Cunha. A.L., Do. M.N., Nonsubsampled contourlet transform: construction and application in enhancement[C], 2005 International Conference on Image Processing,I-469-72.
[140] S.G.Chang, B.Yu, and M. Vetterli, Spatially adaptive wavelet thresholding with context modeling for image denoising[J], IEEE Trans. Image Process., 9(9): 1522–1531, Sep. 2000.
[141] S.G.Chang, B.Yu, and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Trans on Image Processing, 2000, 9(9):1532-1546.
[142]那彦,焦李成.基于多分辨率分析理论的图像融合方法[M].西安:西安电子科技大学出版社.2007.
[143] Harpreet Singh, Jyoti Raj, Gulsheen Kaur. Image Fusion using Fuzzy Logic and Applications [C]. IEEE International Conference on Fuzzy Systems, 2004,337-340.
[144]覃征,鲍复民,李爱国等.多传感器图像融合及其应用综述[J].微电子学与计算机. 2004,21(2):1-5.
[145] George A. Lampropoulos, Yifeng L, Electro-Optical and SAR Image Fusion For Improvements on Target Feature Estimation[J]. SPIE, Applications of Photonic Technology, 2002, 4833:214-225.
[146]刘贵喜,杨万海.基于多尺度对比度塔的图像融合方法及性能评价[J].光学学报.2001,11(11):1336-1442.
[147] M. E. Ulug, M. E., and McCullough, C. L.. A quantitative metric for comparison of night vision fusion algorithms[C]. Sensor Fusion: Architectures, Algorithms, and Applications IV, SPIE Vol. 4051, 2000:80-88.
[148] Sadjadi, F. Comparative Image Fusion Analysais, Computer Vision and Pattern Recognition[C]. 2005 IEEE Computer Society Conference.2005,3:8-8.
[149] Herrington W.F. Jr., Horn B.K.P., Masaki I.. Application of the discrete Haar wavelet transform to image fusion for night-time driving[C]. Intelligent Vehicles Symposium, 2005,6-8 June 2005: 273-277.
[150] Anwaar-uI-Haq, Mirza A.M., Qamar S.. An optimized image fusion algorithm for night-time surveillance and navigation[J]. Emerging Technologies, 2005:138-143.
[151] Wang Z. J., Li D R,Li Q Q. Image Fusion with Wavelets Analysis[J].Journal of Wuhan Technical University of Surveying And Mapping,2000,25(2):137-141.
[152] Ranchin T,Wald L.Different Implementations of the ARSIS Concept to Fulfill Users Needs[C].The 2nd GRSSflSPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas,2003: 299-304.
[153] Jiang XY, Bunke H. Edge detection in range images based on scan line approximation[J]. Computer Vision and Image Understanding, 1999,73(2):183-199.
[154]邹谋炎.反卷积和信号复原[M].北京:国防工业出版社,2001.
[155]杨镠,郭宝龙,倪伟.基于区域特性的Contourlet域多聚焦图像融合算法[J].西安交通大学学报.41(4),2007.
[2] Keenan E, Wright R.G., Zgol M. Infrared laser imaging for circuit board and IC failure detection[C].AUTOTESTCON (Proceedings), Future Sustainment for Military and Aerospace, 2003:399-406.
[3]周鹏,王明时,陈书旺等.基于红外图像处理的埋地石油管道自动探测技术[J].天津大学学报. 2007, 40(1):88-93.
[4]刘建都,张善文,李续武.基于脊波变换的一种红外图像增强技术[J].宇航计测技术. 2007, 27(2):62-64.
[5] E.F.J. Ring. The historical development of temperature measurement in medicine[J]. Infrared Physics & Technology. 2007,49(3):297-301.
[6]胡斌.医用红外热图处理及辅助诊断系统的设计[D].天津:天津大学.2006.
[7] W.K.Prat.Digital Image Porcessing,2nd Ed.John Wiley,New York,1991.
[8]章毓晋.图像工程(上册)-图像处理和分析[M].北京:清华大学出版社,1999.
[9]高鑫.基于小波变换和PDE模型噪声与模糊图像恢复[D].北京:北京师范大学.2001.
[10]王超.基于变分问题和偏微分方程的图像处理技术研究[D].合肥:中国科技大学.2007.
[11]薛琴.基于多尺度分析的图像增强研究[D].南昌:江西师范大学.2006.
[12]王胜军.图像增强方法的研究[D].大连:大连海事大学.2005.
[13] Y.S.Choi and R.Krishnapuram,A Robust Approach to Image Enhancement Based on Fuzzy Logic[J],IEEE Transaction on Image Processing,6(6),1997.
[14]容观澳.计算机图像处理[M].北京:清华大学出版社,2000.
[15]张悦.基于模糊逻辑和小波理论的图像增强算法研究[D].保定:华北电力大学, 2007.
[16]张红慧.基于多尺度小波域相关性的图像去噪与增强方法的研究[D].西安:西北大学,2007.
[17] McCauley H. Image enhancement of infrared uncooled focal plane array imagery[J].Proceedings of the SPIE - The International Society for Optical Engineering,Infrared Systems and Components III.1050:28-34,1989.
[18] Chun Moo Lo.Forward looking infrared (FLIR) image enhancement for the automatic target cuer system. Proceedings of the Society of Photo-Optical Instrumentation Engineers[J], Image Processing for Missile Guidance. 1980 , 238:91-102.
[19] Silverman, Jerry. Signal-processing algorithms for display and enhancement of IR images[J].Proc. SPIE-- The International Society for Optical Engineering. 1993,2020:440-450.
[20] Virgil M,Vickers E. Plateau equalization algorithm for rea1-time display of high-quality infrared imagery[J]. Optical Engineering. 35(7): 1921-1926, 1996.
[21] John B Zimmerman. An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement[J]. Transactions on Medical Imaging. 1988, 7(4):304-312.
[22] Jadwiga Rogowska,Kendall Preston,and Donald Sashin. Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs[J]. IEEE Trans on Biomedical Engineering. 1988, 35(10):817-827.
[23]陈文建,迟泽英.红外烟幕遮蔽条件下目标热像的增强[J].红外技术. 2003,25(4):66-69.
[24]邸慧,于起峰,张小虎.一种基于灰度变换的红外图像增强算法[J].应用光学.2006,1(6):12-14.
[25]殷德奎.张保民.柏连发.红外图像的二维灰度变换增强方法[J].红外技术.1999,21(3):25-29.
[26] Kamel Belkace-Boussaid and Azeddine Beghdadi,A New Image Smoothing Method Based on a Simple Model of Spatial Processing in the Early Stages of Human Vision[J], IEEE Transactions on Image Processing, 9(2): 220-226,2000.
[27] Yajun Fang, Keiichi Yamada,Yoshiki Ninomiya, Berthold K.P.Horn, Ichiro Masaki, A Shape-Independent Method for Pedestrian Detection With Far-infrared Images[J], IEEE Transations on Vehicular Technology. 2004, 53(6): 1679-1697.
[28] N. Scales, C. Herry, M. Frize. Automated Image Segmentation for Breast Analysis Using Infrared Images[C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS. 2004,1737-1740.
[29]向健勇,徐军.一种实用的红外图像分割算法研究[J].西安电子科技大学学报, 1997,24(3):416-420.
[30]林晓春,王艳.一种基于图像融合的红外图像增强的新方法[J].红外技术. 2004,26(2):48-53.
[31]王磊.红外图像增强算法研究及其实时实现技术[D].西安:西安电子科技大学.2007.
[32] Rafael C.Gonzalez,Richard E.Woods, Digital Image Processing, Second Edition , Publishing House of Electronics Industry. 2002,148-215.
[33] Jinshan Tang and Scot Acton,Image Enhancement Using a Contrast Measure in the Compressed Domain[J],IEEE Signal Processing Leters,Oct 2003, 10(10):289-292.
[34] Stephane Mallat, Sifen Zhong. Characterization of Signal from Multiscale Edges[J],IEEE Transaction on Patenr Analysis and Machine In telligence, 1992, 14(7):710-732.
[35]王耀南,李树涛,毛建旭.计算机图像处理与识别技术[M],北京:高等教育出版社,2001,91-96.
[36] Tamar Peli, Jee S. Lim. Adaptive filtering for image enhancement[J], Optical Engineering,1982,21(1):108-112.
[37] H.Pege McAdams, G. Allan Johnson, S.A. Suddarth, C.E.Ravin. Implementation of adaptive filtration for digital chest imaging[J], Optical Engineering, 1987, 26(7): 669-673.
[38] Pabio G. Tahoces, Jose Correa, Miguel Sauto, Carmen Gonzalez. Enhancement of chest and breast radiographs by automatics patial filtering[J], IEEE Trans on Medical Imaging, 1991, 10(1):330-335.
[39] Haiguang Chen, Andrew Li, Leon Kaufman,and James Hale. A fast filtering algorithm for image enhancement[J], IEEE Trans on Medical Imaging, 1997, 13(3): 557-564.
[40]宫武鹏,王永仲.一种基于小波变换的红外图像对比度增强技术[J].国防科技大学学报.2000,22(6):117-119.
[41]张瑾.基于小波变换的红外图像处理应用研究[D].成都:西南交通大学.2006.
[42] Raghuveer M.R. Image enhancement and denoising by wavelet transform for concealed weapon detection[J]. Proceedings of the SPIE - The International Society for Optical Engineering. 1997, 2942: 112-122.
[43] Sadjadi Firooz A. Infrared target detection with probability density functions of wavelet transform subbands[J].Applied Optics. 2004,43(2):315-323.
[44] Richard Alan Peters, A New Algorithm for Image Noise Reduction using Mathematical Morphology[J], IEEE Trans. on Image Processing, 1995,4(3):554-568.
[45] J G M Schavemaker. Image Sharpening by Morphological Filtering[J]. Pattern Recognition. 2000, 33:997-1012.
[46]冯国进,顾国华,陈钱.基于形态学的红外图像边缘增强[J].激光与红外.2003, 33(6):453-454.
[47] King I.,Xu L.Adaptive contrast enhancement by entropy maximization with a 1-K-1 constrained network[C].Proceedings of International Conference on Neural Information Processing (ICONIP '95), 1995, 2(2):703-706.
[48] F.Russo and G.Rampom,Edge Extraction by FIRE Operators[C],In Proc.Third IEEE Int.Conf.Fuzzy Systems, Orlando.FL.1994,pp.249-253.
[49] Kamel Belkacem-Boussaid and Azeddine Beghdadi,A New Image Smoothing Method Based on a simple model of Spatial Processing in the Early Stages of Human Vision[J], IEEE Trans. on Image Processing, 2000,9(2):220-226.
[50] Hollingworth G.,Tyrrell A. and Smith S.Simulation of Evolvable Hardware to Solve Low Level Image Processing Tasks[J].LNCS-Springer,Vol.1596:46-58,1999.
[51] Jain A.Fundamentals of Digital Image Processing,Prentice Hall,1991.
[52] Nakao Z.,Takashibu G.,Ali F.E. and Chen Y.W.,Evolutionary CT Image Reconstruction[C].Proceedings of ICNN'97,1997,1608-1611.
[53]过润秋,李俊峰,林晓春.基于并行遗传算法的红外图像增强及相关技术[J].西安电子科技大学学报(自然科学版).2004,31(1):6-8,20.
[54]朱菊华,杨新,李俊等.基于纹理分析的保细节平滑滤波器[J].中国图形图像学报. 2001,6(11):11-15.
[55] Tony F.chan,Stanley Osher,Jianhong Shen,The Digital TV Filter and Nonlinear Denoising[J]. IEEE Trans. on Image Proeessing. 2001,10(2): 231-241.
[56] Tony F.Chan,Luminita A.Vese, Active Contours Without Edges[J].IEEE Trans.on Image Processing. 2001,10(2): 266-277.
[57]王超.基于变分问题和偏微分方程的图像处理技术研究[D].合肥:中国科学技术大学.2007.
[58]林晓春,李存志.一种基于图像融合的红外图像增强新方法[J].西安电子科技大学学报.2005,32(2):189-192,215.
[59]吉书鹏,丁晓青.可见光与红外图像增强融合算法研究[J].红外与激光工程.2002,31(6):518-521,544.
[60] Yang J, Blum R S. Image Fusion Using the Expectation-maximization Algorithm and a Hidden Markov Models [C].IEEE Vehicular Technology Conference. Los Angeles: IEEE, 2004,6:4563-4567.
[61]毛士艺,赵巍.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.
[62]刘建都,张善文,李续武.基于脊波变换的一种红外图像增强技术[J].宇航计测技术.2007,27(2):62-64.
[63] E.J.Candes. Ridgelets: Theory and application[D]. Stanford University, 1998.
[64] J.L.Starck,E.Candes,and D.L.Donoho,The Curvelet Transform for Image Denoising[J]. IEEE Trans.Image Processing, 2002, 11:131-141.
[65] E.L.Pennec,S.Mallat. Sparse geometric image representation with bandelets[J]. IEEE Transcations on Image Processing, 2005,14(4):423-438.
[66] G.Peyre,S.Mallat. Discrete bandelets with geometric orthogonal filters[C]. IEEE International Conference on Image Processing, Genoa, Italy.2005:65-68.
[67] Romberg J.K., Wakin M., Baraniuk R., Multiscale wedgelet image analysis: fast decompositions and modeling[C]. Proceedings 2002 International Conference on Image Processing. 2002, vol.3:585-588.
[68] Wu Ru-Shan, Chen Ling. Directional illumination analysis using beamlet decomposition and propagation[J]. Geophysics. 2006,71(4):147-159.
[69] Minh N. Do., Martin Vetterli.The Contourlet Transform: An Efficient Directional Multiresolution Image Representation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2005, 14(12):2091-2106.
[70]李智杰.稀疏信号表示理论及其在图像增强中的应用[M].西安:西安电子科技大学.2005.
[71]李洪均,梅雪,林锦国.基于Contourlet域HMT模型的红外图像去噪算法[J].红外技术.2007,29(6):357-360.
[72] Belbachir A.N., Goebel P.M..A combined multiresolution approach for faint source extraction from infrared astronomical raw images sequence[J].2005 IEEE/SP 13th Workshop on Statistical Signal Processing (IEEE Cat. No. 05TH8839) .419-424 Vol.1,2006.
[73] Kamel Belkacem-Boussaid and Azeddine Beghdadi,A New Image Smoothing Method Based on a simple model of Spatial Processing in the Early Stages of Human Vision[J], IEEE Trans. on Image Processing,9(2):220-226,2000.
[74] Rafael C.Gonzalez,Richard E. Woods.数字图像处理(第二版)[M].北京:电子工业出版社. 2003.
[75]陈传波,金先级.数字图像处理[M].北京:机械工业出版社.2004.
[76]邵凡麒.红外图像处理[D].南京:南京理工大学,2005.
[77]李宏贵,李兴国,李国祯等.一种基于遗传算法的红外图像增强方法[J]. Systems Engineering and Electronics.1999,21(7):44-46.
[78]练秋生.基于视觉特性的多方向小波构造及其应用研究[D].秦皇岛:燕山大学.2006.
[79] Mallat, Stephane G. Theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1989,11(7):674-693.
[80]何锦平.基于小波多分辨分析的图像增强及其应用研究[D].西安:西北工业大学,2003.
[81]王博.数字图像处理方法与应用研究[D].西安:西北工业大学, 1998.
[82]张日华,黄彦明.小波变换及其在图像处理中的特性分析[J].中国图像图形学报.1997,2(7).
[83]侯波.基于小波变换消除遥感图像噪声[D].北京:中国科学院遥感应用研究所, 2002.
[84]王建勇.二维图像降噪、特征检测的算法及实用化技术研究[D].北京:北京邮电大学,2005.
[85]魏政刚等.图像质量评价方法的历史、现状和未来[J].中国图像图形学报.1998,3(5):236-239.
[86]殷晓丽,方向忠,翟广涛.一种JPEG图片的无参考图像质量评价方法[J].计算机工程与应用.2006,18(3):79-81,129.
[87] CCIR Rec. 500-5."Method for the subjective assessment of the quality of television pictures", Recommendations of the ITU, Telecommunications Standardization Sector, 1990.
[88]谢晋.基于Contourlet变换图像去噪算法研究[D].西安:西北工业大学,2007.
[89]焦李成,谭山.图像的多尺度方向分析:回顾与展望[J].电子学报. 2003, 31(12A) :1975-1981.
[90] F.G. Meyer, R.R.Coifman. Brushlets: a tool for directional image analysis and image compression[J]. Journal of Appl. and Comput. Harmonic Analysis, 1997, 5: 147-187.
[91] A.Cohen, B.Matei. Compact representation of images by edge adapted multiscale transforms IEEE Int. Conf Image Process., Thessaloniki, Greece, 2001:8-11.
[92] D.L.Donoho. Wedgelets: Nearly-minimax estimation of edges[J]. Ann. Statist, 1999, 27 :859-897.
[93] J.Romberg, M.Wakin,R. Baraniuk. Approximation and Compression of Piecewise Smooth Images Using a Wavelet/Wedgelet Geometric Model[C]. IEEE Intenrational Conference on Image Processing,Barcelona,Spain,2003:49-52.
[94] E.L.Pennec,S. Mallat.Sparse geometric image repersentation with bandelets[J]. IEEE Trans.Image Process.,2005,14(4):423-438.
[95] G. Peyre,S.Mallat.Discerte bandelets with geometric orthogonal filters[C]. IEEE Intenrational Confeernce on Image Processing, Genoa, Italy,2005:65-68.
[96] V.Velisavljevic, B.Beferull-Lozano, M.Vetterli, P.L.Dragotti. Approximation power of directionlets[C], IEEE Intenrational Conference on Image Processing, Genoa, Italy, 2005:741-744.
[97] E.J.Candes. Ridgelets: Theory and application[D]. Stanford University, 1998:20-55.
[98] E.J.Candes. Harmonic analysis of neural networks[J]. Applied and Computational Harmonic Analysis, 1999,6(2):197-218.
[99] D.L.Donoho. Orthonormal ridgelets and linear singularities. [Stanford University, Report], 1998: 1-15.
[100] M.N.Do, M.Vetterli, Orthonormal finite ridgelet transform for image compression[C], IEEE Intenrational Conference on Image compression, Vancouver, Canada, 2000:367-370.
[101]侯彪,刘芳,焦李成.基于脊波变换的直线特征检测[J].中国科学(E辑). 2003,33(1):65-73.
[102] E.J.Candes, D.L.Donoho. Curvelets-a surprisingly effective nonadaptive representation for objects with edges. In: Cohen A., RabutC., Schumaker L.L. editors. Curve and surface fitting. Saint-Malo: Vanderbilt University Press, 1999:105-120.
[103] J.L.Starck, E.J.Candes, D.L.Donoho. The curvelet transform for image denoising[J]. IEEE Transaction on Image Processing, 2002, 11(6): 670-684.
[104] E.J.Candes, F.Guo. New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction[J]. Signal Processing, 2002, 82(11):1519-1543.
[105] E.J.Candes, D L Donoho. Digital Curvelet transform via USFFT. [Stanford University, Report], 2004:1-35.
[106] M.N.Do, M. Veterli. Contourlets and sparse image expansions[C]. Proceedings of The Intenrational Society for Optical Engineering, San Diego, USA, 2003: 560-570.
[107] M.N.Do, M.Veterli. The contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Transaction on Image Porcessing, 2005,14(12):2091-2106.
[108] D.L.Donoho, M. Vetterli, R.A.DeVore. Data compression and harmonic analysis[J]. IEEE Trans. Information Theory, 1998,44(6):2435-2476.
[109] A. Skodras, C. Christopoulos, T. Ebrahimi.The JPEG 2000 still image compression standard[J]. IEEE Signal Processing Magazine, 2001,18:36–58.
[110]唐永茂.多尺度方向分析在图像处理中的应用研究[D].上海:上海交通大学,2005.
[111] Minh N. Do, Member, Martin Vetterli. The Contourlet Transform: An Efficient Directional Multiresolution Image Representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[112]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法[J].计算机应用研究, 2006,9(20): 18-22.
[113] Ramin Eslami, Hayder Radha. The Contourlet Transform for Image De-noising Using Cycle Spinning[C]. Conference Record of the Asilomar Conference on Signals, Systems and Computers. 2003: 1982-1986.
[114] Burt P.J., Adelson E.H. The Laplacian pyramid as a compact image code[J], IEEE Trans. Commun., 1983, 31(4): 532-554.
[115] R.H.Bambegrer, M.J.T.Smith. A filter bank for the directional decomposition of images: Theory and design[J]. IEEE Trans. Singal Proc. 1992, 40(4):882-893.
[116] P.Vaidyanathan. MultiMate Systems and Filter banks. Englewood Cliffs, NJ :Prentice-Hall,1993.
[117] R.Ansari.m. Efficient IIR and FIR fan filters[J]. IEEE Trans. Circuits Syst.,1987:941-945.
[118] R.H.Bamberger,M.J.T.Smith.A filter bank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Proc., 1992(40):882-893.
[119] M.N.Do.Directional Multiresolution Image Representations[D]. Department of Communication System. Swiss Federal Insittute of Technology Lausanne,2001.
[120]苗启广.多传感器图像融合方法研究.博士学位论文.西安电子科技大学,西安,2005.
[121] A Gersho, B.Ramaurthi. Image coding using vector quantization[C]. IEEE Int. Conf.Acoust, speed signal Processing. 1982,428-431.
[122] Stromberg J.O. A modfied Franklin system and higher order spline systems on R.n. as uncondition bases for Hardy spaces[J].Trans.Amer.Math.Soc.1982,475-494.
[123] Gabor D. Theory of communication[J]. Journal of Institute for Electrical Engineering, SPIE, 1946,93:429-457.
[124] Meyer Y. Wavelets and operators. Cambridge.UK,1992,3(2):45-64.
[125] Mallet S. Multi-resolution approximations and wavelet orthonormal bases of L2(R)[J]. Transaction of American Mathematical Society, 1989, 315(1): 69-87.
[126] Mallat S. A theory for multiresolution signal decomposition[J].IEEE Trans. on PAMI,1989,11(7):674-693.
[127] Daubechies I.Orthonormal bases of compactly supported wavelets[J]. Comm. Pure Appl.Math.,1988,41:909-996.
[128] C K Chui,J Z Wang.A cardinal spline approach to wavelets [J].Proc Amer Math Soc,1991,113:785-793.
[129] Coifman R R,Wickerhauser M W.Entropy-based algorithms for best basis selection [J].IEEE Transactions on Information Theory,1992,38(2):713-718.
[130] S.G.Chang, B.Yu, and M. Vetterli, Spatially adaptive wavelet thresholding with context modeling for image denoising[J]. IEEE Trans. Image Process., 2000, 9(9):1522-1531.
[131] S.G.Chang, B.Yu, and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Trans on Image Processing, 2000, 9(9): 1532-1546.
[132] R. R. Coifman, D. L. Donoho. Translation-invariant de-noising[C]. Wavelets and Statistics, Springer Lecture Notes in Statistics 103, New York: Springer,Verlag, 1995. 125-150.
[133] Alyson K. Fletcher, Kannan Ramchandran, Vivek K. Goyal. Wavelet denoising by recursive cycle spinning[C]. Proc. IEEE International Conference Image Processing, Rochester, NY: 2002. 873-876.
[134] Eslami R, Radha H. The contourlet transformfor image de-noising using cycle spinning[C]. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2003. 1982-1986.
[135] A.L.Cunha, J.Zhou, Minh.N.Do.The nonsubsampled contourlet:theory, design and applications[J]. IEEE Transactions on Image Processing.2006, 15(10):1779-1793.
[136]章鹏.图像的方向多分辨率分析[D].合肥:中国科学技术大学, 2006.
[137] Cunha Arthur L., Do Minh N.Filter design for directional multiresolution decomposition[J]. Proceedings of SPIE- The International Society for Optical Engineering, Wavelets XI, 2005, 5914:1-10.
[138] Da Cunha Arthur L.,Zhou, J., Do, Minh N.The nonsubsampled contourlet transform: Theory, design, and applications.IEEE Transactions on Image Processing. 2006,15(10):3089-3101.
[139] Jianping Zhou,Cunha. A.L., Do. M.N., Nonsubsampled contourlet transform: construction and application in enhancement[C], 2005 International Conference on Image Processing,I-469-72.
[140] S.G.Chang, B.Yu, and M. Vetterli, Spatially adaptive wavelet thresholding with context modeling for image denoising[J], IEEE Trans. Image Process., 9(9): 1522–1531, Sep. 2000.
[141] S.G.Chang, B.Yu, and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Trans on Image Processing, 2000, 9(9):1532-1546.
[142]那彦,焦李成.基于多分辨率分析理论的图像融合方法[M].西安:西安电子科技大学出版社.2007.
[143] Harpreet Singh, Jyoti Raj, Gulsheen Kaur. Image Fusion using Fuzzy Logic and Applications [C]. IEEE International Conference on Fuzzy Systems, 2004,337-340.
[144]覃征,鲍复民,李爱国等.多传感器图像融合及其应用综述[J].微电子学与计算机. 2004,21(2):1-5.
[145] George A. Lampropoulos, Yifeng L, Electro-Optical and SAR Image Fusion For Improvements on Target Feature Estimation[J]. SPIE, Applications of Photonic Technology, 2002, 4833:214-225.
[146]刘贵喜,杨万海.基于多尺度对比度塔的图像融合方法及性能评价[J].光学学报.2001,11(11):1336-1442.
[147] M. E. Ulug, M. E., and McCullough, C. L.. A quantitative metric for comparison of night vision fusion algorithms[C]. Sensor Fusion: Architectures, Algorithms, and Applications IV, SPIE Vol. 4051, 2000:80-88.
[148] Sadjadi, F. Comparative Image Fusion Analysais, Computer Vision and Pattern Recognition[C]. 2005 IEEE Computer Society Conference.2005,3:8-8.
[149] Herrington W.F. Jr., Horn B.K.P., Masaki I.. Application of the discrete Haar wavelet transform to image fusion for night-time driving[C]. Intelligent Vehicles Symposium, 2005,6-8 June 2005: 273-277.
[150] Anwaar-uI-Haq, Mirza A.M., Qamar S.. An optimized image fusion algorithm for night-time surveillance and navigation[J]. Emerging Technologies, 2005:138-143.
[151] Wang Z. J., Li D R,Li Q Q. Image Fusion with Wavelets Analysis[J].Journal of Wuhan Technical University of Surveying And Mapping,2000,25(2):137-141.
[152] Ranchin T,Wald L.Different Implementations of the ARSIS Concept to Fulfill Users Needs[C].The 2nd GRSSflSPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas,2003: 299-304.
[153] Jiang XY, Bunke H. Edge detection in range images based on scan line approximation[J]. Computer Vision and Image Understanding, 1999,73(2):183-199.
[154]邹谋炎.反卷积和信号复原[M].北京:国防工业出版社,2001.
[155]杨镠,郭宝龙,倪伟.基于区域特性的Contourlet域多聚焦图像融合算法[J].西安交通大学学报.41(4),2007.