窗口经验模式分解及其在图像处理中的应用
|
摘要
经验模式分解(Empirical Mode Decomposition, EMD)算法是一种新的信号处理技术。EMD可有效地将非线性非平稳信号中每一点的时(空)局部振荡模式提取出来,得到一系列内蕴模式函数分量(Intrinsic Mode Function, IMF),经Hilbert变换后,使得瞬时频率有了确切的物理意义。傅立叶变换能够在频域内得到非常高的分辨率,但是在时(空)域内却失去了分辨能力。由于窗口傅立叶变换的窗函数是固定的,它只提供了有限的时(空)频分辨率。小波变换具有多尺度多分辨能力,在不同尺度下的时(空)域和频域内同时具有较好的分辨率,但由于基函数是固定的,对分析的结果产生不利的影响。EMD分解依靠数据自身局部时间尺度特性进行分解。与小波变换相比,EMD不但具有小波变换的多尺度多分辨能力,而且消除了小波变换的缺陷,使得IMF分量可有效地反映出原信号的物理特征。因此,EMD比傅立叶变换和小波变换更有优势。
由于EMD算法是靠经验得到的,缺陷不可避免：一、算法的运行速度很慢；二、无法解决两个频率相差很大的信号在一定的条件下叠加后不能够分离的问题,称其为“信号隐藏”。影响EMD算法在图像处理中的应用。本文通过对EMD算法缺陷成因的探讨,提出了一种新的经验模式分解算法---窗口经验模式分解。实验表明,新算法较之已有的算法具有优越性。我们将新算法应用到数字图像处理领域中,结果显示它在数字图像处理领域应用中具有潜力。
本文主要工作与创新点有：
一.针对二维EMD算法中的缺陷,分析成因,研究解决方法。新算法利用窗函数思想,配合EMD算法的结构,提出一种新的经验模式分解---窗口经验模式分解(Window Empirical Mode Decomposition, WEMD), WEMD算法有两大优点：一,算法的运行速度比传统的算法要快得多,能满足工程应用的需要；二,解决了传统算法无法解决的“信号隐藏”问题。
二.传统的二维EMD算法得到的分解图像由于“信号隐藏”问题,使得分解图像中不可避免要产生灰斑,影响了它的应用范围。本文根据WEMD算法的高频信息强获取能力和多尺度多分辨率特性,研究该算法在数字图像中的应用：
A.图像边缘提取：根据第一个IMF分量图像具有很好的边缘特性,提出了两种边缘提取算法：1)直接利用阈值提取IMF图像边缘；2)利用二维Hilbert变换和非极大值抑制算法提取图像边缘。
B.图像融合：利用WEMD算法的多尺度多分辨率特性和高频细节信息的强获取能力,运用本文提出的背景／细节融合规则对前几层IMF分量进行处理,将融合后的IMF分量重构得到融合图像。
C.图像去噪：根据噪声图像经WEMD分解得到IMF分量图像中的噪声呈现斑点噪声的特性,运用Gamma滤波对前几层IMF分量图像进行处理,将处理后的IMF分量重构得到去噪图像。
D.图像增强：根据IMF分量图像的直方图服从正态分布的特性,运用直方图匹配算法,使IMF分量图像中的细节得到增强,将增强后的IMF分量重构得到增强图像。以上提出的基于WEMD的图像处理算法,实验证明无论是运用主观分析,还是运用客观分析,其效果都优于已有的算法。
三.结合密码学思想和EMD算法的结构,将每一层IMF分量筛选停止条件“SD”值作为密钥,运用不同的SD值得到每一层IMF分量。用同样大小的秘密图像代替其中的一个IMF分量,最后将IMF分量重构得到含有秘密图像的载体图像。载体图像满足人眼视觉系统,外人无法分辨出载体图像中是否含有秘密信息。当需要恢复秘密图像时,秘密图像可被恢复。
Empirical Mode Decomposition (EMD) is a new technology of signal processing. EMD can decompose the nonlinear and non-stationary signals into a series of intrinsic mode functions (IMF) which has local oscillation mode of every point in time/space domain. After Hilbert transformation, the obtained instrantaneous frequency data will give a better understanding of the physics. Fourier transformation can obtain a better resolution in the frequency domain, but not in the time/space domain. Window Fourier transformation can only obtain a limited time/space-frequency resolution, because window function is fixed. Wavelet transformation has capability of multi-scale and multi-resolution, and can obtain a better resolution simultaneously in the time/space and frequency domains at different scales. However, because the basic function is fixed, it is disadvantageous to the results of analysis. EMD is based on the local characteristic time scale of the data. Compared with wavelet, EMD has all advantages of wavelets, and dispels the drawback of wavelets, so IMF can accurately reflect physical characteristics of the signals. Therefore, Hilbert-Huang transformation is superior to Fourier and wavelet.
The drawback of EMD algorithm is inevitable, because EMD algorithm is based on experience. First、Calculating speed of EMD is very slow; Second、EMD can't resolve "information hiding" problem, which is that two mixed signals with great frequency difference can't be separated under certain conditions. They will influence the area of application in image processing. A new EMD algorithm---Window Empirical Mode Decomposition (WEMD) is proposed to solve problems. The experiments indicate that the new algorithm is superior to the existing algorithms. After the new algorithm is applied to digital image processing, the results show that they have the potential in digital image processing fields.
The main innovation and work are: 1. For solving the drawbacks in 2D-EMD, new method of EMD...WEMD based on the window function and on the EMD's frame is proposed. The WEMD possesses both advantages:first, the algorithm possesses high calculating speed; second, the algorithm has solved the "information hiding" problem, which is difficult to be solved in traditional EMD algorithm.
2. The IMF images of the traditional 2-D EMD algorithm are inevitable to produce gray spots for "signal hiding", so its field of application is limited. My thesis will utilize WEMD's ability in the acquirement of the high frequency data and its capability of multi-scale and multi-resolution to study the algorithms in digital image applications:
A. Image edge extraction:Two methods of edge extraction are proposed by making use of the characteristic of the first IMF image which has the good edge. One utilizes the threshold to extact the edge; the other utilizes the Hilbert transformation and non-maxima suppression to extact the edge.
B. Image fusion:The ability in the acquirement of the high frequency data and the capability of multi-scale and multi-resolution of WEMD are uitilized. After modifying the IMF components with the proposed fusion rule, the fusion image can be obtained by adding up the modified IMFs and the residual component.
C. Image denoising:Because the noise in IMF images represents speckle noise, Gammma filter is applied to modify the IMF components. The denoise image can be obtained by adding up the modified IMFs and the residual component.
D. Image enhancement:For the histogram of IMF image following normal distribution, the first few IMF images may be modified by histogram matching to enhance the IMF images. The enhanced image will be obtained by adding up the modified IMFs and the residual component. The experiments have shown that the proposed algorithms are efficient in image processing and better than the existing algorithms.
3. According to EMD's frame and the cryptographic thinking, the SD value which is the sift stop condition of every IMF is as key. After the every IMF will be obtained by utilizing the different SD, one IMF will be replaced with secret image of equal size. The image with secret image will be obtained by adding up the IMFs and the residual component. When secret image needs to be restored, it may be restored.
由于EMD算法是靠经验得到的,缺陷不可避免：一、算法的运行速度很慢；二、无法解决两个频率相差很大的信号在一定的条件下叠加后不能够分离的问题,称其为“信号隐藏”。影响EMD算法在图像处理中的应用。本文通过对EMD算法缺陷成因的探讨,提出了一种新的经验模式分解算法---窗口经验模式分解。实验表明,新算法较之已有的算法具有优越性。我们将新算法应用到数字图像处理领域中,结果显示它在数字图像处理领域应用中具有潜力。
本文主要工作与创新点有：
一.针对二维EMD算法中的缺陷,分析成因,研究解决方法。新算法利用窗函数思想,配合EMD算法的结构,提出一种新的经验模式分解---窗口经验模式分解(Window Empirical Mode Decomposition, WEMD), WEMD算法有两大优点：一,算法的运行速度比传统的算法要快得多,能满足工程应用的需要；二,解决了传统算法无法解决的“信号隐藏”问题。
二.传统的二维EMD算法得到的分解图像由于“信号隐藏”问题,使得分解图像中不可避免要产生灰斑,影响了它的应用范围。本文根据WEMD算法的高频信息强获取能力和多尺度多分辨率特性,研究该算法在数字图像中的应用：
A.图像边缘提取：根据第一个IMF分量图像具有很好的边缘特性,提出了两种边缘提取算法：1)直接利用阈值提取IMF图像边缘；2)利用二维Hilbert变换和非极大值抑制算法提取图像边缘。
B.图像融合：利用WEMD算法的多尺度多分辨率特性和高频细节信息的强获取能力,运用本文提出的背景／细节融合规则对前几层IMF分量进行处理,将融合后的IMF分量重构得到融合图像。
C.图像去噪：根据噪声图像经WEMD分解得到IMF分量图像中的噪声呈现斑点噪声的特性,运用Gamma滤波对前几层IMF分量图像进行处理,将处理后的IMF分量重构得到去噪图像。
D.图像增强：根据IMF分量图像的直方图服从正态分布的特性,运用直方图匹配算法,使IMF分量图像中的细节得到增强,将增强后的IMF分量重构得到增强图像。以上提出的基于WEMD的图像处理算法,实验证明无论是运用主观分析,还是运用客观分析,其效果都优于已有的算法。
三.结合密码学思想和EMD算法的结构,将每一层IMF分量筛选停止条件“SD”值作为密钥,运用不同的SD值得到每一层IMF分量。用同样大小的秘密图像代替其中的一个IMF分量,最后将IMF分量重构得到含有秘密图像的载体图像。载体图像满足人眼视觉系统,外人无法分辨出载体图像中是否含有秘密信息。当需要恢复秘密图像时,秘密图像可被恢复。
Empirical Mode Decomposition (EMD) is a new technology of signal processing. EMD can decompose the nonlinear and non-stationary signals into a series of intrinsic mode functions (IMF) which has local oscillation mode of every point in time/space domain. After Hilbert transformation, the obtained instrantaneous frequency data will give a better understanding of the physics. Fourier transformation can obtain a better resolution in the frequency domain, but not in the time/space domain. Window Fourier transformation can only obtain a limited time/space-frequency resolution, because window function is fixed. Wavelet transformation has capability of multi-scale and multi-resolution, and can obtain a better resolution simultaneously in the time/space and frequency domains at different scales. However, because the basic function is fixed, it is disadvantageous to the results of analysis. EMD is based on the local characteristic time scale of the data. Compared with wavelet, EMD has all advantages of wavelets, and dispels the drawback of wavelets, so IMF can accurately reflect physical characteristics of the signals. Therefore, Hilbert-Huang transformation is superior to Fourier and wavelet.
The drawback of EMD algorithm is inevitable, because EMD algorithm is based on experience. First、Calculating speed of EMD is very slow; Second、EMD can't resolve "information hiding" problem, which is that two mixed signals with great frequency difference can't be separated under certain conditions. They will influence the area of application in image processing. A new EMD algorithm---Window Empirical Mode Decomposition (WEMD) is proposed to solve problems. The experiments indicate that the new algorithm is superior to the existing algorithms. After the new algorithm is applied to digital image processing, the results show that they have the potential in digital image processing fields.
The main innovation and work are: 1. For solving the drawbacks in 2D-EMD, new method of EMD...WEMD based on the window function and on the EMD's frame is proposed. The WEMD possesses both advantages:first, the algorithm possesses high calculating speed; second, the algorithm has solved the "information hiding" problem, which is difficult to be solved in traditional EMD algorithm.
2. The IMF images of the traditional 2-D EMD algorithm are inevitable to produce gray spots for "signal hiding", so its field of application is limited. My thesis will utilize WEMD's ability in the acquirement of the high frequency data and its capability of multi-scale and multi-resolution to study the algorithms in digital image applications:
A. Image edge extraction:Two methods of edge extraction are proposed by making use of the characteristic of the first IMF image which has the good edge. One utilizes the threshold to extact the edge; the other utilizes the Hilbert transformation and non-maxima suppression to extact the edge.
B. Image fusion:The ability in the acquirement of the high frequency data and the capability of multi-scale and multi-resolution of WEMD are uitilized. After modifying the IMF components with the proposed fusion rule, the fusion image can be obtained by adding up the modified IMFs and the residual component.
C. Image denoising:Because the noise in IMF images represents speckle noise, Gammma filter is applied to modify the IMF components. The denoise image can be obtained by adding up the modified IMFs and the residual component.
D. Image enhancement:For the histogram of IMF image following normal distribution, the first few IMF images may be modified by histogram matching to enhance the IMF images. The enhanced image will be obtained by adding up the modified IMFs and the residual component. The experiments have shown that the proposed algorithms are efficient in image processing and better than the existing algorithms.
3. According to EMD's frame and the cryptographic thinking, the SD value which is the sift stop condition of every IMF is as key. After the every IMF will be obtained by utilizing the different SD, one IMF will be replaced with secret image of equal size. The image with secret image will be obtained by adding up the IMFs and the residual component. When secret image needs to be restored, it may be restored.
引文
[1]冈萨雷斯[美],数字图像处理(阮秋琦译),电子工业出版社,2001
[2]章毓晋,图像工程,清华大学出版社,1999
[3]何东建,迪楠,数字图像处理,西安电子科技大学出版社,2003
[4]科恩[美],时频分析：理论与应用(白居宪译),西安交通大学出版社,1998.
[5]文成林,周东华,多尺度估计理论及其应用,清华大学出版社,2002.
[6]I. Daubechies, Tenlectures on wavelet, Philadephia:Society for Industrialand Appl. Math,1992.
[7]S. Mallat, A wavelet tour of signal processing, New York:Academic press,1997.
[8]Norden E. Huang and etc, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London. A 1998, 454:903-995,
[9]Boashash, b., Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals, Proc. IEEE,1992,80:417-430
[10]Bedrosian, E., Aproduct theorem for Hilbert transform, Proc. IEEE 51,1963,868-869
[11]LionCohen, Time-frequency analysis, EnglewoodCliffs, NJ:Prentice-Hall,1995.
[12]Norden E. Huang and S. R. Long and Z. Shen, The mechanism for frequency downshift in nonlinear wave evolution. Adv. Appl. Mech.1996,32:59-111,
[13]Norden E. Huang, C.C. Tung and S.R. Long, the probability structure of the ocean surface, The sea 1990,9:335-366,
[14]Norden E. Huang, Zheng Shen and Steven. R. Long, A new view of nonlinear water wave:The Hilbert Spectrum, Annu. Rev. Fluid Mech.,1999,31:417-457,
[15]D.E. Vakman and L.A. Vainshtein, Amplitude, phase, frequenvy-fundamental concepts of oscillation theory, Sov. Phys. Usp.,1978,20:1002-1016
[16]B. Picinbono, On the instantaneous amplitude and phase of signal, IEEE Trans. on Signal Processing,1997,93:552-560
[17]D. Gabor, Theory of commynications, J. IEEE,1946,93:429-457.
[18]L. Mandel, Interpretation of instantaneous frequency, Amer. J. Phys,1974,42: 840-846,
[19]K. Grochenig, Foundations of time-frequency analysis, Birkhauser, New York,2000.
[20]J. Starch, M. Elad, D. Donoho, Image decomposition:separation of texture from piecewise smooth content, SPIE conference on signal and image processing:wavelet applications in signal and image processing X, SPIE's 48th Annual Meeting, August 2003,35-38
[21]M.N. Do, M. Vetterli, Digital Ridgelet Transform for Image Pepressentation, IEEE Transcation on Image Processing,2001,12:16-28.
[22]D.L. Donoho, M.R. Duncan, Digital Curvelet Transform:Straregy, Implementation and Experiments, Proc. SPIE,2000,4056:12-29.
[23]Minh N Do, Martin Vetterli, the Contourlet Transform:An Efficient Directional Multiresolution Image Representation. IEEE Transcation on Image Processing,2004, 14-16
[24]D. Gabor, Theory of communication, Journal of the Institute of Electrical Engineers, 1946,93:429-457.
[25]B. Boashash, Estimating and interpreting the instantaneous frequency of a signal-Part1:Fundamentals., IEEE Proc,1992,80(4):520-539
[26]B. Boashash, Estimating and interpreting the instantaneous frequency of a signal-Part2: Algorithms and applications. IEEE Proc,1992,80(4):540-568
[27]David L. Donoho, Ana Georgina Flesia. Can recent innovations in harmonic analysis explain key findings in natural image statistics Computation in Neural Systems,2001, 12(3):371-393
[28]焦李成,谭山,图像的多尺度几何分析：回顾和展望,电子学报,2003,12(31)：1975-1981
[29]C. Han, G.H. Wang, C.D. Fan, A novel method toreduce speckle in SAR images, International Journal of Remote Sensing,2002,23:5095-5101.
[30]H.Y. Yue, H.D. Guo, C.M. Han, etal, A SAR interferogram filter based on the empirical mode decomposition method", Geoscience and Remote Sensing Symposium, 2001,5:2061-2063,.
[31]S.R. Long, Use of the empirical mode decomposition and Hilbert-Huang transform in image analysis, World Multi-conference on Systemics, Cybernetics and Informatics, Cybernetics And Informatics:Concepts And Applications (Part Ⅱ),2001.
[32]Z.X. Liu, H.J. Wang, S.L. Peng, Texture segmentation using directional empirical mode decomposition, International Conference on Image Processing,2004,1:279-282,
[33]J. C. Nunes, S. Guyot, E. Delechelle, Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition, Machine Vision and Applications, 2005,60(4):177-188
[34]S. Sinclair, G.S. Pegram, Empirical Mode Decomposition in 2-D space and time:a tool for spacetime rainfall analysis and forecasting, Hydrology and Earth System Sciences Discussions, European Geosciences Union,2005,2:289-318,
[35]Z.H. Yang, D.X. Qi, L.H. Yang, Signal period analysis based on Hilbert-Huang Transform and its application to texture analysis, IEEE Proc. of the Third International Conference on Image and Graphics,2005,430-433,
[36]C.Z. Xiong, J.Y. Xu, J.C. Zou, D.X. Qi, Texture classification based on EMD and FFT, Zhejiang Univ. Science(A),2006,7(9):1516-1521,
[37]H. Stark, An extension of the Hilbert transform product theorm, IEEE Proc,1971, 59:1359-1360
[38]J.P. Havlicek, J.W. Havlicek, D. Ngao, et al,2D Hilbcrt transforms and computed AM-FM models. IEEE Proc,1998,59:602-606
[39]L. Stefan, Hahn, Multidimensional complex signals with singleorthant spectra, IEEE Proc,1992,80(8):1287-1300
[40]B. Thomas, S. Gerald, Hypercomplex signals-a novel extension of the analytic signal to the multidimensional case, IEEE Transaction on Signal Processing,2001,49 (11): 2844—2852
[41]V. Torre, T.A.Poggio, On edge detection, IEEE Trans., Pattern Anal., MaCh. Intell., 1986,8(2):147-163.
[42]I. Sobel, Neighborhood coding of binary images for fast contour following and general array binary Processing, Computer Graphics Image Process,1978,8:127-135.
[43]L.G. Roberts, Machine perception of three dimensional solids, In Optical and Electrooptical Information Processing, MIT Press., Cambridge, MA,1965,159-197.
[44]J.M.S. Prewitt, Object enhancement and extraction, in:B.S. Lipkin, A. Rosenfeld(Eds.), Picture Analysis and Psychopietorics, Academic Press. NewYork,1970,75-149.
[45]W.K. Pratt, Quantitative Design and Evaluation of Enhancement/Thresholding Edge Detectors in Procession, IEEE transaction on image procession,1979,67(5):753-763.
[46]J. Canny, A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[47]T. Aydin, Y.Yemez, E. Anarim, B. Snaqur, Multidetectional and multiscal edge detection via M-band wavelet transform, IEEE Trans. Image Process,2006, 5(9):1370-1377.
[48]S. Mallat, S. Zhong, Characterization of signals from muitiscale edges, IEEE Trans. Pattern Anal. 1.Mach.Intell.1992,14(7):710-732.
[49]敬忠良,肖刚,李振华,图像融合,高等教育出版社,2007
[50]C. Pohl, Multisensor Image Fusionin Remote Sensing:Concepts, Methods and Applicationsm, Remote Sensing,1998,19(5):823-854.
[51]Wang Z., Ziou D., ArmenaKis C., A Comparative analysis of image fusion methods, IEEE Transaction On Geoscience and Remote Sensing,2005,43(6):1391-402
[52]R.C. Luo, M.G.A. Kay, Tutorialon Multisensor Integration and Fusion, IEEE. Intl.Conf. on Industrial Eleetronies, Pacific Grove, CA,1990,1:707-722.
[53]M.A. Abidi, R.C.Gonzalez, Data Fusionin Robotics and Machine Intelligence, San Diego:Academic Press. Inc.1992
[54]E. Lallier, Real-time Pixel-level Image Fusion through Adaptive Weight Averaging, Technical Report, Royal Military Collage of Canada,1999.
[55]V. Shetigara, A Generalised Component Substitution Technique for Spatial Enhancement of Multispectral Image Using a Higher Resolution Data Set, Photogrammetric Engineering and Remote Sensing,1992,58(5):561-567.
[56]R.K. Sharma, M. Pavel, Adaptive and Statistical Image Fusion, Society for Information Display Digest of technical Papers,1996,27:969-972.
[57]W.A Wright, Fast Image Fusion with A Markov Random Field, Proe. Int. Conf. on Image Processing and Its Applications, Stevenage, IEEE,1999,557--561.
[58]F.S. Melgani, B. Sebestiano, G. Vemazza, Fusion of Multiremporal Contextual Information by Neural Networks for Multisensor Remote Sensing Image Classification, Integrated Computer-Aided Engineering,2003,10(1):81-90.
[59]A. Toet, A Morphologieal Pyramidal Image Decomposition, Pattern Recognition Lerters,1989,9(4):255-261.
[60]A. Toet, Multiscale Contrast Enhancement with Applications to Image Fusion, Optical Engineering,199,31(5):1026-1031.
[61]P.J. Burt, R.J. Kolezynski, Enhanced Image Capture through Fusion, Proc. Of 4th International Conf. on Computer Vision (ICCV), Berlin, Germany, May,1993, 173-182.
[62]H. Li, B. S. Manjunath and S. K. Mitra., Multisensor Image Fusion Using the Wavelet Transform, Graphical Models and Image Processing,1995,57(3):235-245
[63]M.N. Do, M. Vetterli, Image denoising using orthonormal finite ridgelet transform, Proceedings of SPIE, the International Society for Optical Engineering,2000, 4119(2):831-842
[64]M. Choi, R.Y. Kim, M.G. Kim, The curvelet transform for image fusion, IEEE Geoscience and Remote sensing letters,2005,2:136-140
[65]Aboubaker M. ALEjaily, Ibrahim A. El Rube, Fusion of Remote Sensing Images Using Contourlet Transform, Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering,2008,213-218
[66]李树涛,王耀南,张昌凡,多传感器图像融合的客观评价与分析,仪器仪表学报,2002,23(6)：651-654
[67]刘贵喜,杨万海,基于小波分解的图像融合方法及性能评价,自动化学报,2002,28(6)：927-934
[68]王海晖,彭嘉雄,吴巍,等.多源遥感图像融合效果评价方法研究.计算机工程与应用,2003,39(25)：33-37
[69]Z. Zhang, R.S. Blum, A Categorization of multiscale decomposition based image fusion schemes with a Performance study for a digital camera application, Proceeding of the IEEE,1999,87(8):1315-1326.
[70]M. Gonzalez-Audicana, et al. Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition, IEEE Transaction on Geoscience and Remote Sensing,2004,42(6):1291-1299
[71]S.G. Chang, B. Yu, M. Vetterli, Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE transaction on image procession,2000,9 (9):1532-1546
[72]Minh N. Do, Martin Vetterli, Image denoising using orthonormal finite ridgelet transform, Wavelet Applications in Signal and Image Processing, Proc. SPIE,2000, 4119:247-251
[73]B. Matalon, M. Elad, M. Zibulevsky, Image Denoising With The Contourlet Transform, Proceedings of SPARSE'05,2005,545-549
[74]J.L. Starck, E.J. Candes, D.L. Donoho, The Curvelet Transform for Image Denoising, IEEE transaction on image procession,2002,11 (6):670-684
[75]J.S. Lee, Digital image enhancement and noise filtering by use of local statistics, IEEE Trans. Pattern Analysis and Machine Intelligence,1980,2(2):165-168
[76]D.T. Kuan, A.A. Sawchuk, T.C. Strand, and P. Chavel, Adaptive noise smoothing filter for images with signal-dependent noise, IEEE Trans. Pattern Analysis and Machine Intelligence,1985,7(2):165-177
[77]V.S. Frost, J.A. Stiles, K.S. Shanmugan, and J.C. Holtzman, A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. Pattern Analysis and Machine Intelligence,1982,4(2):157-166
[78]A. Lopes, E. Nezry, R. Touzi, and H. bur, Maximum a posteriori filtering and first order texture models in SAR images, IGARSS'90, Washington D.C.,1990,2409-2412.
[79]Z. Shi, K.B. Fung, A Comparison of digital speckle filters, Proceedings of Geoscience and Remote Sensing Symposium Paris, IEEE Publications,1994:2129—2131.
[80]R. A. Hununel, Image enhancement by histogram transformation, Computer Vision, Graphics and Image Processing (CVGIP),1977:184-195.
[81]J. Rogowska, K. Preston, D. Sashin, Evaluation of digital unsharp masking and local contrast stretching applied to chest radiographs, IEEE Trans on Biomedical Engineering,1988,35(10):817-827.
[82]John S. Daponte and Martin D. Fox, Enhancement of chest radiographs with gradient operators, IEEE Trans on Medical Imaging,1988,7(2):109-117
[83]T. Peli and J.S. Lim, Adaptive filtering for image enhancement, Optical Engineering, 1982,21(1):108-112.
[84]H. PegemeAdams, G. AllanJohnson, S.A. Suddarth, R.H. Sherrier, C.E. Ravin. Implementation of adaptive filtration for digital chest imaging, Optical Engineering, 1987,26(7):669-673.
[85]Pabio G. Talloees, Jose Correa, Miguel Sauto, Carmen Gonzalez, Enhancement of chest and breast radiographs by automatic spatial filtering, IEEE Trans on Medical Imaging,1991,10(1):330-335.
[86]Haiguang Chen, Andrew Li, Leon Kaufman, and James Hale, A fast filtering algorithm for image enhancement, IEEE Trans on Medical Imaging,1997, 13(3):557-564
[87]X.H. Wang, S.H. lstepanian and YH. Song, Microarry Image Enhancement By Using Stationary Wavelet Transform, IEEE Transaction on Nanobioscience,2003, 2(4):184-189.
[88]D. Herie, B. Potocnik., Image Enhancement by Using Directional Wavelet Transform, 28th Int. Conf. Information Technology Interfaces ITI,2006, Cavtat, Croatia.
[89]J. Zhou, A.L. Cunha and M.N. Do, Nonsubsampled contourlet transform:construction and application in enhancement, Proe. Of IEEE Intl. Conf. on Image Processing, Genoa. Italy, SeP.2005:469--472.
[90]J.L Starek etc, Gray and Color Image Contrast Enhancement by the Curvelet Transform, IEEE Transaction on Image Processing,2003,12(6):706-717.
[91]K.V. Velde, Multi-scale color image enhancement, in Proc. Int. Conf. Image Processing,1999,3:584-587
[92]Li Ke, Ming-Hui Zhang, Lian-Qing Huang, CR Image Enhancement with Wavelet Transform, Journal of Optoelectronics-Laser(光电子·激光),2005,16:989-992. (in Chinese)
[93]J.C. FU, J.W. CHAI, T.C. WONG, Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic in magnetic resonance images, Magnetic Resonance Imaging,2000,18:1135-1141.
[94]Ingemat J. Cox, Matthew L. Miller,数字水印(王颖,黄志蓓译),电子工业出版社,2003
[95]杨义先,钮心忻,数字水印理论与技术,高等教育出版社,2006
[96]Turner L. F., Digital data security system, Patent IPNW089/08915,1989
[97]Bender W., Gruhl D., Morimoto N., Techniques for data hiding, IBM System Journal, 1996,35:313-336
[98]Nikolaidis A., Pitas I., Optimal detector structure for DCT and subband domain watermarking, International Conference on Image Processing,2002,3:465-468
[99]Tang W. L., Aoki Y., A DCT-based coding of images in watenmarking, Proceedings of international Conference on Information, Communication and Signal Processing, 1997,1:510-512
[100]余鹏飞,刘兵,基于离散余弦变换的大容量信息隐藏盲提取算法,计算机应用,2006,26(4)：815-817
[101]F.A.P. Petitcolas, R.J. Anderson, and M.G. Kuhn, Information hiding-a survey, Proceedings of IEEE, special issue on protection of multimedia content,1999, 87(7):1062-1078
[102]I. J. Cox and M. L. Miller, The first 50 years of electronic watermarking, J. Appl. Signal Process,2002,2:126-132.
[103]Victor H. G., Clara C.R., Mariko N. M., Watermarking algorithm based on the DWT, IEEE Latin America Transactions,2006,4(4):257-267
[104]易开祥,石教英,自适应二维数字水印系统,计算机图形图像学报,2001, 6(5):444-449
[2]章毓晋,图像工程,清华大学出版社,1999
[3]何东建,迪楠,数字图像处理,西安电子科技大学出版社,2003
[4]科恩[美],时频分析：理论与应用(白居宪译),西安交通大学出版社,1998.
[5]文成林,周东华,多尺度估计理论及其应用,清华大学出版社,2002.
[6]I. Daubechies, Tenlectures on wavelet, Philadephia:Society for Industrialand Appl. Math,1992.
[7]S. Mallat, A wavelet tour of signal processing, New York:Academic press,1997.
[8]Norden E. Huang and etc, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London. A 1998, 454:903-995,
[9]Boashash, b., Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals, Proc. IEEE,1992,80:417-430
[10]Bedrosian, E., Aproduct theorem for Hilbert transform, Proc. IEEE 51,1963,868-869
[11]LionCohen, Time-frequency analysis, EnglewoodCliffs, NJ:Prentice-Hall,1995.
[12]Norden E. Huang and S. R. Long and Z. Shen, The mechanism for frequency downshift in nonlinear wave evolution. Adv. Appl. Mech.1996,32:59-111,
[13]Norden E. Huang, C.C. Tung and S.R. Long, the probability structure of the ocean surface, The sea 1990,9:335-366,
[14]Norden E. Huang, Zheng Shen and Steven. R. Long, A new view of nonlinear water wave:The Hilbert Spectrum, Annu. Rev. Fluid Mech.,1999,31:417-457,
[15]D.E. Vakman and L.A. Vainshtein, Amplitude, phase, frequenvy-fundamental concepts of oscillation theory, Sov. Phys. Usp.,1978,20:1002-1016
[16]B. Picinbono, On the instantaneous amplitude and phase of signal, IEEE Trans. on Signal Processing,1997,93:552-560
[17]D. Gabor, Theory of commynications, J. IEEE,1946,93:429-457.
[18]L. Mandel, Interpretation of instantaneous frequency, Amer. J. Phys,1974,42: 840-846,
[19]K. Grochenig, Foundations of time-frequency analysis, Birkhauser, New York,2000.
[20]J. Starch, M. Elad, D. Donoho, Image decomposition:separation of texture from piecewise smooth content, SPIE conference on signal and image processing:wavelet applications in signal and image processing X, SPIE's 48th Annual Meeting, August 2003,35-38
[21]M.N. Do, M. Vetterli, Digital Ridgelet Transform for Image Pepressentation, IEEE Transcation on Image Processing,2001,12:16-28.
[22]D.L. Donoho, M.R. Duncan, Digital Curvelet Transform:Straregy, Implementation and Experiments, Proc. SPIE,2000,4056:12-29.
[23]Minh N Do, Martin Vetterli, the Contourlet Transform:An Efficient Directional Multiresolution Image Representation. IEEE Transcation on Image Processing,2004, 14-16
[24]D. Gabor, Theory of communication, Journal of the Institute of Electrical Engineers, 1946,93:429-457.
[25]B. Boashash, Estimating and interpreting the instantaneous frequency of a signal-Part1:Fundamentals., IEEE Proc,1992,80(4):520-539
[26]B. Boashash, Estimating and interpreting the instantaneous frequency of a signal-Part2: Algorithms and applications. IEEE Proc,1992,80(4):540-568
[27]David L. Donoho, Ana Georgina Flesia. Can recent innovations in harmonic analysis explain key findings in natural image statistics Computation in Neural Systems,2001, 12(3):371-393
[28]焦李成,谭山,图像的多尺度几何分析：回顾和展望,电子学报,2003,12(31)：1975-1981
[29]C. Han, G.H. Wang, C.D. Fan, A novel method toreduce speckle in SAR images, International Journal of Remote Sensing,2002,23:5095-5101.
[30]H.Y. Yue, H.D. Guo, C.M. Han, etal, A SAR interferogram filter based on the empirical mode decomposition method", Geoscience and Remote Sensing Symposium, 2001,5:2061-2063,.
[31]S.R. Long, Use of the empirical mode decomposition and Hilbert-Huang transform in image analysis, World Multi-conference on Systemics, Cybernetics and Informatics, Cybernetics And Informatics:Concepts And Applications (Part Ⅱ),2001.
[32]Z.X. Liu, H.J. Wang, S.L. Peng, Texture segmentation using directional empirical mode decomposition, International Conference on Image Processing,2004,1:279-282,
[33]J. C. Nunes, S. Guyot, E. Delechelle, Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition, Machine Vision and Applications, 2005,60(4):177-188
[34]S. Sinclair, G.S. Pegram, Empirical Mode Decomposition in 2-D space and time:a tool for spacetime rainfall analysis and forecasting, Hydrology and Earth System Sciences Discussions, European Geosciences Union,2005,2:289-318,
[35]Z.H. Yang, D.X. Qi, L.H. Yang, Signal period analysis based on Hilbert-Huang Transform and its application to texture analysis, IEEE Proc. of the Third International Conference on Image and Graphics,2005,430-433,
[36]C.Z. Xiong, J.Y. Xu, J.C. Zou, D.X. Qi, Texture classification based on EMD and FFT, Zhejiang Univ. Science(A),2006,7(9):1516-1521,
[37]H. Stark, An extension of the Hilbert transform product theorm, IEEE Proc,1971, 59:1359-1360
[38]J.P. Havlicek, J.W. Havlicek, D. Ngao, et al,2D Hilbcrt transforms and computed AM-FM models. IEEE Proc,1998,59:602-606
[39]L. Stefan, Hahn, Multidimensional complex signals with singleorthant spectra, IEEE Proc,1992,80(8):1287-1300
[40]B. Thomas, S. Gerald, Hypercomplex signals-a novel extension of the analytic signal to the multidimensional case, IEEE Transaction on Signal Processing,2001,49 (11): 2844—2852
[41]V. Torre, T.A.Poggio, On edge detection, IEEE Trans., Pattern Anal., MaCh. Intell., 1986,8(2):147-163.
[42]I. Sobel, Neighborhood coding of binary images for fast contour following and general array binary Processing, Computer Graphics Image Process,1978,8:127-135.
[43]L.G. Roberts, Machine perception of three dimensional solids, In Optical and Electrooptical Information Processing, MIT Press., Cambridge, MA,1965,159-197.
[44]J.M.S. Prewitt, Object enhancement and extraction, in:B.S. Lipkin, A. Rosenfeld(Eds.), Picture Analysis and Psychopietorics, Academic Press. NewYork,1970,75-149.
[45]W.K. Pratt, Quantitative Design and Evaluation of Enhancement/Thresholding Edge Detectors in Procession, IEEE transaction on image procession,1979,67(5):753-763.
[46]J. Canny, A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[47]T. Aydin, Y.Yemez, E. Anarim, B. Snaqur, Multidetectional and multiscal edge detection via M-band wavelet transform, IEEE Trans. Image Process,2006, 5(9):1370-1377.
[48]S. Mallat, S. Zhong, Characterization of signals from muitiscale edges, IEEE Trans. Pattern Anal. 1.Mach.Intell.1992,14(7):710-732.
[49]敬忠良,肖刚,李振华,图像融合,高等教育出版社,2007
[50]C. Pohl, Multisensor Image Fusionin Remote Sensing:Concepts, Methods and Applicationsm, Remote Sensing,1998,19(5):823-854.
[51]Wang Z., Ziou D., ArmenaKis C., A Comparative analysis of image fusion methods, IEEE Transaction On Geoscience and Remote Sensing,2005,43(6):1391-402
[52]R.C. Luo, M.G.A. Kay, Tutorialon Multisensor Integration and Fusion, IEEE. Intl.Conf. on Industrial Eleetronies, Pacific Grove, CA,1990,1:707-722.
[53]M.A. Abidi, R.C.Gonzalez, Data Fusionin Robotics and Machine Intelligence, San Diego:Academic Press. Inc.1992
[54]E. Lallier, Real-time Pixel-level Image Fusion through Adaptive Weight Averaging, Technical Report, Royal Military Collage of Canada,1999.
[55]V. Shetigara, A Generalised Component Substitution Technique for Spatial Enhancement of Multispectral Image Using a Higher Resolution Data Set, Photogrammetric Engineering and Remote Sensing,1992,58(5):561-567.
[56]R.K. Sharma, M. Pavel, Adaptive and Statistical Image Fusion, Society for Information Display Digest of technical Papers,1996,27:969-972.
[57]W.A Wright, Fast Image Fusion with A Markov Random Field, Proe. Int. Conf. on Image Processing and Its Applications, Stevenage, IEEE,1999,557--561.
[58]F.S. Melgani, B. Sebestiano, G. Vemazza, Fusion of Multiremporal Contextual Information by Neural Networks for Multisensor Remote Sensing Image Classification, Integrated Computer-Aided Engineering,2003,10(1):81-90.
[59]A. Toet, A Morphologieal Pyramidal Image Decomposition, Pattern Recognition Lerters,1989,9(4):255-261.
[60]A. Toet, Multiscale Contrast Enhancement with Applications to Image Fusion, Optical Engineering,199,31(5):1026-1031.
[61]P.J. Burt, R.J. Kolezynski, Enhanced Image Capture through Fusion, Proc. Of 4th International Conf. on Computer Vision (ICCV), Berlin, Germany, May,1993, 173-182.
[62]H. Li, B. S. Manjunath and S. K. Mitra., Multisensor Image Fusion Using the Wavelet Transform, Graphical Models and Image Processing,1995,57(3):235-245
[63]M.N. Do, M. Vetterli, Image denoising using orthonormal finite ridgelet transform, Proceedings of SPIE, the International Society for Optical Engineering,2000, 4119(2):831-842
[64]M. Choi, R.Y. Kim, M.G. Kim, The curvelet transform for image fusion, IEEE Geoscience and Remote sensing letters,2005,2:136-140
[65]Aboubaker M. ALEjaily, Ibrahim A. El Rube, Fusion of Remote Sensing Images Using Contourlet Transform, Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering,2008,213-218
[66]李树涛,王耀南,张昌凡,多传感器图像融合的客观评价与分析,仪器仪表学报,2002,23(6)：651-654
[67]刘贵喜,杨万海,基于小波分解的图像融合方法及性能评价,自动化学报,2002,28(6)：927-934
[68]王海晖,彭嘉雄,吴巍,等.多源遥感图像融合效果评价方法研究.计算机工程与应用,2003,39(25)：33-37
[69]Z. Zhang, R.S. Blum, A Categorization of multiscale decomposition based image fusion schemes with a Performance study for a digital camera application, Proceeding of the IEEE,1999,87(8):1315-1326.
[70]M. Gonzalez-Audicana, et al. Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition, IEEE Transaction on Geoscience and Remote Sensing,2004,42(6):1291-1299
[71]S.G. Chang, B. Yu, M. Vetterli, Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE transaction on image procession,2000,9 (9):1532-1546
[72]Minh N. Do, Martin Vetterli, Image denoising using orthonormal finite ridgelet transform, Wavelet Applications in Signal and Image Processing, Proc. SPIE,2000, 4119:247-251
[73]B. Matalon, M. Elad, M. Zibulevsky, Image Denoising With The Contourlet Transform, Proceedings of SPARSE'05,2005,545-549
[74]J.L. Starck, E.J. Candes, D.L. Donoho, The Curvelet Transform for Image Denoising, IEEE transaction on image procession,2002,11 (6):670-684
[75]J.S. Lee, Digital image enhancement and noise filtering by use of local statistics, IEEE Trans. Pattern Analysis and Machine Intelligence,1980,2(2):165-168
[76]D.T. Kuan, A.A. Sawchuk, T.C. Strand, and P. Chavel, Adaptive noise smoothing filter for images with signal-dependent noise, IEEE Trans. Pattern Analysis and Machine Intelligence,1985,7(2):165-177
[77]V.S. Frost, J.A. Stiles, K.S. Shanmugan, and J.C. Holtzman, A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. Pattern Analysis and Machine Intelligence,1982,4(2):157-166
[78]A. Lopes, E. Nezry, R. Touzi, and H. bur, Maximum a posteriori filtering and first order texture models in SAR images, IGARSS'90, Washington D.C.,1990,2409-2412.
[79]Z. Shi, K.B. Fung, A Comparison of digital speckle filters, Proceedings of Geoscience and Remote Sensing Symposium Paris, IEEE Publications,1994:2129—2131.
[80]R. A. Hununel, Image enhancement by histogram transformation, Computer Vision, Graphics and Image Processing (CVGIP),1977:184-195.
[81]J. Rogowska, K. Preston, D. Sashin, Evaluation of digital unsharp masking and local contrast stretching applied to chest radiographs, IEEE Trans on Biomedical Engineering,1988,35(10):817-827.
[82]John S. Daponte and Martin D. Fox, Enhancement of chest radiographs with gradient operators, IEEE Trans on Medical Imaging,1988,7(2):109-117
[83]T. Peli and J.S. Lim, Adaptive filtering for image enhancement, Optical Engineering, 1982,21(1):108-112.
[84]H. PegemeAdams, G. AllanJohnson, S.A. Suddarth, R.H. Sherrier, C.E. Ravin. Implementation of adaptive filtration for digital chest imaging, Optical Engineering, 1987,26(7):669-673.
[85]Pabio G. Talloees, Jose Correa, Miguel Sauto, Carmen Gonzalez, Enhancement of chest and breast radiographs by automatic spatial filtering, IEEE Trans on Medical Imaging,1991,10(1):330-335.
[86]Haiguang Chen, Andrew Li, Leon Kaufman, and James Hale, A fast filtering algorithm for image enhancement, IEEE Trans on Medical Imaging,1997, 13(3):557-564
[87]X.H. Wang, S.H. lstepanian and YH. Song, Microarry Image Enhancement By Using Stationary Wavelet Transform, IEEE Transaction on Nanobioscience,2003, 2(4):184-189.
[88]D. Herie, B. Potocnik., Image Enhancement by Using Directional Wavelet Transform, 28th Int. Conf. Information Technology Interfaces ITI,2006, Cavtat, Croatia.
[89]J. Zhou, A.L. Cunha and M.N. Do, Nonsubsampled contourlet transform:construction and application in enhancement, Proe. Of IEEE Intl. Conf. on Image Processing, Genoa. Italy, SeP.2005:469--472.
[90]J.L Starek etc, Gray and Color Image Contrast Enhancement by the Curvelet Transform, IEEE Transaction on Image Processing,2003,12(6):706-717.
[91]K.V. Velde, Multi-scale color image enhancement, in Proc. Int. Conf. Image Processing,1999,3:584-587
[92]Li Ke, Ming-Hui Zhang, Lian-Qing Huang, CR Image Enhancement with Wavelet Transform, Journal of Optoelectronics-Laser(光电子·激光),2005,16:989-992. (in Chinese)
[93]J.C. FU, J.W. CHAI, T.C. WONG, Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic in magnetic resonance images, Magnetic Resonance Imaging,2000,18:1135-1141.
[94]Ingemat J. Cox, Matthew L. Miller,数字水印(王颖,黄志蓓译),电子工业出版社,2003
[95]杨义先,钮心忻,数字水印理论与技术,高等教育出版社,2006
[96]Turner L. F., Digital data security system, Patent IPNW089/08915,1989
[97]Bender W., Gruhl D., Morimoto N., Techniques for data hiding, IBM System Journal, 1996,35:313-336
[98]Nikolaidis A., Pitas I., Optimal detector structure for DCT and subband domain watermarking, International Conference on Image Processing,2002,3:465-468
[99]Tang W. L., Aoki Y., A DCT-based coding of images in watenmarking, Proceedings of international Conference on Information, Communication and Signal Processing, 1997,1:510-512
[100]余鹏飞,刘兵,基于离散余弦变换的大容量信息隐藏盲提取算法,计算机应用,2006,26(4)：815-817
[101]F.A.P. Petitcolas, R.J. Anderson, and M.G. Kuhn, Information hiding-a survey, Proceedings of IEEE, special issue on protection of multimedia content,1999, 87(7):1062-1078
[102]I. J. Cox and M. L. Miller, The first 50 years of electronic watermarking, J. Appl. Signal Process,2002,2:126-132.
[103]Victor H. G., Clara C.R., Mariko N. M., Watermarking algorithm based on the DWT, IEEE Latin America Transactions,2006,4(4):257-267
[104]易开祥,石教英,自适应二维数字水印系统,计算机图形图像学报,2001, 6(5):444-449
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |