置换混叠图像的盲分离及应用研究
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摘要
盲信号分离是只根据观测到的信号来分离或恢复出未知源信号的过程,是近年来信号处理领域和神经网络领域研究的热点之一,在图像处理,语音识别等方面有着许多潜在的应用。目前,人们在盲源分离方面做了很多有益的研究,根据观测信号与源信号的数目关系,可分为超定、适定、欠定以及单通道盲分离,其中以单通道盲分离最具有挑战性。以往研究者研究的单通道盲分离问题大都假设多个源信号之间是以叠加的方式进行混叠的,然而在实际应用中,可能观测到的一类混合信号不是以叠加方式,而是以置换的方式与另外的信号发生混叠,由于
这两种混合方式完全不同,现有的盲分离方法就不能用来解决这种单通道的置换混叠信号的分离问题。本文围绕置换混叠图像的盲分离问题展开研究,并将这种置换盲分离思想引入到图像篡改检测研究中,为目前广泛研究的图像被动取证提供新的解决方案。
论文的主要工作概括如下:
1、首次对单通道的置换混叠图像盲分离问题展开深入研究。根据置换混叠图像的定义给出了其数学模型,并在置换混合矩阵为特殊二值矩阵(0、1矩阵)的基础上,提出了基于混合矩阵估计的置换混叠盲分离模型。同时,针对不同的源图像,我们提出了一系列基于参数估计的单通道盲分离方法。
2、在源信号为JPEG图像的前提下,针对JPEG置换混叠图像盲分离问题,提出了基于压缩因子估计的盲分离方法。该方法利用JPEG压缩子块被相同压缩因子再次压缩前后基本保持不变的特性,根据再次压缩前后图像子块的相关性大小估计出原始压缩因子,并以此构造一个映射空间,在映射空间利用压缩因子参数的不同分类实现了置换混合矩阵的估计,从而实现了源图像子块的分离。最后,将该盲分离方法应用到JPEG篡改图像的盲检测中,仿真结果表明该方法的有效性和鲁棒性。
3、在源信号为模糊图像的条件下,利用模糊置换混叠图像的频域特性估计出模糊参数,提出了两种基于盲复原的盲分离方法。对于散焦模糊置换混叠图像,采用对角线分析方法来估计散焦模糊半径R ,再用Lucy-Richardson(L-R)算法对置换混叠图像进行盲复原,并通过定义像素梯度绝对值和来对复原产生的振铃效应进行评价,根据评价结果进行分类估计出置换混合矩阵,从而完成置换源图像子块的分离。对于运动模糊置换混叠图像,则利用Radon变换和极值检测估计出点扩展函数的运动方向和长度,再利用L-R算法对置换图像进行盲复原,然后,根据对复原产生的振铃效应进行评价估计出置换混合矩阵,进而恢复出源图像子块。最后,将上述两种基于模糊参数估计的盲分离方法应用到图像的被动取证中,实验结果表明篡改区域均能被准确的检测定位出来。
4、在假定置换混叠图像中包含插值图像的基础上,提出了一种插值因子估计算法来实现插值放大置换混叠图像的盲分离。该方法利用有限差分算法来对插值引入的周期性进行检测,根据周期特性的异同估计出原始插值因子,并根据估计出的插值因子构造一个映射空间,在映射空间利用插值因子参数的可分性实现了置换混合矩阵的估计,从而实现源图像的分离。最后,利用基于插值因子估计的盲分离方法来实现插值放大篡改图像的盲检测,仿真结果表明了上述方法不但具有更好的噪声和JPEG压缩鲁棒性,而且具有更低的算法复杂度。
5、在针对经过模糊处理后的置换混叠图像,提出一种基于二次模糊相关性的单通道盲分离方法。该方法通过对置换混叠图像进行二次模糊,估计二次模糊前后对应子块的相关系数来构造一个映射空间,利用映射空间内参数的不同分类完成置换混合矩阵的估计,从而实现置换源图像子块的分离。最后,将上述盲分离方法应用到经过模糊后处理的篡改图像盲检测中,文中实验结果表明上述方法对经历不同模糊后处理的篡改图像均能达到较高的检测正确率,同时,算法对高斯噪声和有损JPEG压缩都具有较好的鲁棒性。
Blind sources separation (BSS), which became an active research topic in signal processing and neural network in the last decade, consists of separating a set of unknown signals from a set of linear mixtures of them, when no knowledge is available about the mixing coefficients. There are many potential exciting applications of blind sources separation in science and technology, especially in image processing and speech recognition. According to some hypotheses on the number of sources and the number of observed signals, BSS is divided into overdetermined blind sources separation (OBSS), welldetermined blind sources separation (WBSS), underdetermined blind sources separation (UBSS) and single channel blind signal separation (SCBSS). The SCBSS is comparing with multiple channel blind separation, which has more challenging. In the earlier research of the SCBSS, generally, most of the methods to SCBSS assume that the mixing mode of individual source signals is superposition. However, if the mixed mode is permutation, a quite different one from superposition, the approaches mentioned above will no longer be functional. In this dissertation, we investigate the BSS problem of the permuted image and introduce the thought into image tamper detection. The primary contributions of the dissertation are summarized blow:
1. A first and in-depth study on the BSS problem of the permuted image. The mathematical model of the permuted image is obtained by its definition. According to the permutation mixing matrix is a special binary matrix, of which each entry equals 0 or 1, a BSS model of the permuted image is described by estimating the mixing matrix. For different source images, a series of single-channel blind separation algorithms are proposed based on parameters estimation.
2. When the sources are JPEG images, a blind separation method based on compression factors estimation is proposed for permuted JPEG image. The permuted image is compressed again and the primary compression factors are estimated by calculating the correlation coefficients of image blocks with before and after recompression. Using the estimated compression factors, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed method is used in JPEG image tamper detection. Simulation results show the validity and robustness of the proposed algorithm.
3. When the sources are blurred images, two novel single-channel blind separation algorithms for permuted blurred image are proposed by using blind restoration. For permuted defocus blurred image, the defocus blur radius is estimated by the characteristics of permuted image in the frequency domain, and then the permuted image is restored by performing the Lucy-Richardson(L-R)blind restoration method. The ringing effect of restored image is measured by defining the sum of absolute pixel gradient, and the permutation mixing matrixes can be accurately estimated by classifying the ringing effect of each sub-block, thereby separating the source images. For permuted motion blurred image, both the motion direction and length of point spread function (PSF) are estimated by Radon transformation and extrema detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrixes can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. At last, two proposed algorithms are applied to image passive forensics. Simulation results show the tamper regions can be accurately detected.
4. When the sources contain interpolated images, a novel blind separation method based on interpolation factors estimation is proposed for permuted interpolated image. The periodic property of difference sequence is detected by finite-difference for permuted image, according to the periodic property, the various interpolation factor could be identified. Using the estimated interpolation factors, a mapping space is constructed. The permutation mixing matrixes can be estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed method is used in interpolation image tamper detection. Simulation results show the validity and robustness of the proposed algorithm. Compared with existing ones, the algorithm has simple principle and small computational load.
5. For blurring permuted images, a single-channel blind separation scheme using double blur correlation is proposed. The permuted image is blurred again and the correlation coefficients of image blocks are estimated before and after double blurring. Using the estimated correlation coefficients, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed algorithm is applied to image tamper detection with blurring. Simulation results show high detection accuracy for tamper images with various blurring operations. The proposed method has good robustness against Gaussian noise and lossy JPEG compression.
这两种混合方式完全不同,现有的盲分离方法就不能用来解决这种单通道的置换混叠信号的分离问题。本文围绕置换混叠图像的盲分离问题展开研究,并将这种置换盲分离思想引入到图像篡改检测研究中,为目前广泛研究的图像被动取证提供新的解决方案。
论文的主要工作概括如下:
1、首次对单通道的置换混叠图像盲分离问题展开深入研究。根据置换混叠图像的定义给出了其数学模型,并在置换混合矩阵为特殊二值矩阵(0、1矩阵)的基础上,提出了基于混合矩阵估计的置换混叠盲分离模型。同时,针对不同的源图像,我们提出了一系列基于参数估计的单通道盲分离方法。
2、在源信号为JPEG图像的前提下,针对JPEG置换混叠图像盲分离问题,提出了基于压缩因子估计的盲分离方法。该方法利用JPEG压缩子块被相同压缩因子再次压缩前后基本保持不变的特性,根据再次压缩前后图像子块的相关性大小估计出原始压缩因子,并以此构造一个映射空间,在映射空间利用压缩因子参数的不同分类实现了置换混合矩阵的估计,从而实现了源图像子块的分离。最后,将该盲分离方法应用到JPEG篡改图像的盲检测中,仿真结果表明该方法的有效性和鲁棒性。
3、在源信号为模糊图像的条件下,利用模糊置换混叠图像的频域特性估计出模糊参数,提出了两种基于盲复原的盲分离方法。对于散焦模糊置换混叠图像,采用对角线分析方法来估计散焦模糊半径R ,再用Lucy-Richardson(L-R)算法对置换混叠图像进行盲复原,并通过定义像素梯度绝对值和来对复原产生的振铃效应进行评价,根据评价结果进行分类估计出置换混合矩阵,从而完成置换源图像子块的分离。对于运动模糊置换混叠图像,则利用Radon变换和极值检测估计出点扩展函数的运动方向和长度,再利用L-R算法对置换图像进行盲复原,然后,根据对复原产生的振铃效应进行评价估计出置换混合矩阵,进而恢复出源图像子块。最后,将上述两种基于模糊参数估计的盲分离方法应用到图像的被动取证中,实验结果表明篡改区域均能被准确的检测定位出来。
4、在假定置换混叠图像中包含插值图像的基础上,提出了一种插值因子估计算法来实现插值放大置换混叠图像的盲分离。该方法利用有限差分算法来对插值引入的周期性进行检测,根据周期特性的异同估计出原始插值因子,并根据估计出的插值因子构造一个映射空间,在映射空间利用插值因子参数的可分性实现了置换混合矩阵的估计,从而实现源图像的分离。最后,利用基于插值因子估计的盲分离方法来实现插值放大篡改图像的盲检测,仿真结果表明了上述方法不但具有更好的噪声和JPEG压缩鲁棒性,而且具有更低的算法复杂度。
5、在针对经过模糊处理后的置换混叠图像,提出一种基于二次模糊相关性的单通道盲分离方法。该方法通过对置换混叠图像进行二次模糊,估计二次模糊前后对应子块的相关系数来构造一个映射空间,利用映射空间内参数的不同分类完成置换混合矩阵的估计,从而实现置换源图像子块的分离。最后,将上述盲分离方法应用到经过模糊后处理的篡改图像盲检测中,文中实验结果表明上述方法对经历不同模糊后处理的篡改图像均能达到较高的检测正确率,同时,算法对高斯噪声和有损JPEG压缩都具有较好的鲁棒性。
Blind sources separation (BSS), which became an active research topic in signal processing and neural network in the last decade, consists of separating a set of unknown signals from a set of linear mixtures of them, when no knowledge is available about the mixing coefficients. There are many potential exciting applications of blind sources separation in science and technology, especially in image processing and speech recognition. According to some hypotheses on the number of sources and the number of observed signals, BSS is divided into overdetermined blind sources separation (OBSS), welldetermined blind sources separation (WBSS), underdetermined blind sources separation (UBSS) and single channel blind signal separation (SCBSS). The SCBSS is comparing with multiple channel blind separation, which has more challenging. In the earlier research of the SCBSS, generally, most of the methods to SCBSS assume that the mixing mode of individual source signals is superposition. However, if the mixed mode is permutation, a quite different one from superposition, the approaches mentioned above will no longer be functional. In this dissertation, we investigate the BSS problem of the permuted image and introduce the thought into image tamper detection. The primary contributions of the dissertation are summarized blow:
1. A first and in-depth study on the BSS problem of the permuted image. The mathematical model of the permuted image is obtained by its definition. According to the permutation mixing matrix is a special binary matrix, of which each entry equals 0 or 1, a BSS model of the permuted image is described by estimating the mixing matrix. For different source images, a series of single-channel blind separation algorithms are proposed based on parameters estimation.
2. When the sources are JPEG images, a blind separation method based on compression factors estimation is proposed for permuted JPEG image. The permuted image is compressed again and the primary compression factors are estimated by calculating the correlation coefficients of image blocks with before and after recompression. Using the estimated compression factors, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed method is used in JPEG image tamper detection. Simulation results show the validity and robustness of the proposed algorithm.
3. When the sources are blurred images, two novel single-channel blind separation algorithms for permuted blurred image are proposed by using blind restoration. For permuted defocus blurred image, the defocus blur radius is estimated by the characteristics of permuted image in the frequency domain, and then the permuted image is restored by performing the Lucy-Richardson(L-R)blind restoration method. The ringing effect of restored image is measured by defining the sum of absolute pixel gradient, and the permutation mixing matrixes can be accurately estimated by classifying the ringing effect of each sub-block, thereby separating the source images. For permuted motion blurred image, both the motion direction and length of point spread function (PSF) are estimated by Radon transformation and extrema detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrixes can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. At last, two proposed algorithms are applied to image passive forensics. Simulation results show the tamper regions can be accurately detected.
4. When the sources contain interpolated images, a novel blind separation method based on interpolation factors estimation is proposed for permuted interpolated image. The periodic property of difference sequence is detected by finite-difference for permuted image, according to the periodic property, the various interpolation factor could be identified. Using the estimated interpolation factors, a mapping space is constructed. The permutation mixing matrixes can be estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed method is used in interpolation image tamper detection. Simulation results show the validity and robustness of the proposed algorithm. Compared with existing ones, the algorithm has simple principle and small computational load.
5. For blurring permuted images, a single-channel blind separation scheme using double blur correlation is proposed. The permuted image is blurred again and the correlation coefficients of image blocks are estimated before and after double blurring. Using the estimated correlation coefficients, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed algorithm is applied to image tamper detection with blurring. Simulation results show high detection accuracy for tamper images with various blurring operations. The proposed method has good robustness against Gaussian noise and lossy JPEG compression.
引文
[1] Jutten C, Herault J. Blind separation of sources, Part 1: An adaptive algorithm based on neuromimetic architecture [J]. Signal Processing, 1991, 24 (1): 1-10.
[2]张烨.欠定混合信号的盲分离[D].上海:上海大学, 2009.
[3]杨祖元.盲信号分离算法分析及应用研究[D].广州:华南理工大学, 2009.
[4]周郭许.盲信号分离若干关键问题研究[D].广州:华南理工大学, 2010.
[5] Casanovas A L, Monaci G, Vandergheynst P. Blind audiovisual source separation using sparse representations [C]. Presented at IEEE International Conference on Image Processing, 2007.
[6] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries [J]. IEEE Trans. Image Processing, 2006, 15(12): 3736-3745.
[7] Bobin J, Starck J L, Fadili J, et al. Sparsity and morphological diversity in blind source separation [J]. IEEE Trans. Image Processing, 2007, 16(11): 2662-2674.
[8] Makeig S, Jung T P, Bell A J, et al. Blind separation of auditory event-related brain responses into independent components [J]. Proceedings of the National Academy of Sciences of the United States of America, 1997, 94(20): 10979-10984.
[9] Mitianoudis N, Davies M E. Audio source separation of convolutive mixtures [J]. IEEE Trans. Speech and Audio Processing, 2003, 11(5): 489-497.
[10] Lin Q.H., Yin F.L., Mei T.M., et al. A blind source separation based method for speech encryption [J]. IEEE Trans. Circuits and System I, 2006, 53(6): 1320-1328.
[11] Li S, Li C, Lo K.-T, et al. Cryptanalyzing an encryption scheme based on blind source separation [J]. IEEE Trans. Circuits and System I, 2008, 55(4): 1055-1063.
[12] Talwar S, Paulraj A. Blind separation of synchronous co-channel digital signals using an antenna array .2. Performance analysis [J]. IEEE Transactions on Signal Processing, 1997, 45(3): 706-718.
[13] Sahlin H. and Broman H. Separation of real-world signals [J]. Signal Processing, 1998, 64(2): 103-113.
[14] Ristaniemi T. and Joutsensalo Y. Independent component analysis with code information utilization in DS-CDMA signal separation [C]. Presented at Seamless Interconnection for Universal Services, 1999.
[15] Cichocki A, Amari S. Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications [M]. Wiley, 2003.
[16] Flexer A,Bauer H,Pripfl J,et al. Using ICA for removal of ocular artifacts in EEG recorded from blind subjects [J]. Neural Networks, 2005, 18(7):998-1005.
[17] Moosmann M, Eiehele T, Nordby H, et al. Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation. Inernational Journal of Psyehophysiology. 2008,67(3): 212-221.
[18] Common P. Independent component analysis, A new concept? [J]. Signal Processing, Elsevier, 1994, 36 (3): 287-314.
[19] Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis [M]. New York: Wiley, 2001.
[20] Bell A, Sejnowski T. An information-maximization approach to blind separation and blind deconvolution [J]. Neural Computation, 1995, 7 (6): 1129-1159.
[21] Cardoso J F, Laheld B H. Equivariant adaptive source separation [J]. IEEE Transactions on Signal Processing, 1996, 44 (12): 3017-3030.
[22] Belouchrani A, AbedMeraim K, Cardoso J F, et al. A blind source separation technique using second-order statistics [J]. IEEE Transactions on Signal Processing, 1997, 45 (2): 434-444.
[23] Hyvarinen A, Oja E. A fast fixed-point algorithm for independent component analysis [J]. Neural Computation, 1997, 9 (7): 1483-1492.
[24] Cardoso J F. Blind signal separation: Statistical principles [J]. Proceedings of the IEEE, 1998, 86 (10): 2009-2025.
[25] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. IEEE Transactions on Neural Networks, 1999, 10 (3): 626-634.
[26] Hyvarinen A. "The FastICA Matlab package. V2.5," 2005.
[27] Cardoso J F. Infomax and maximum likelihood for blind source separation [J]. IEEE Signal Processing Letters, 1997, 4 (4): 112-114.
[28] Pearlmutter B, Parra L. A context-sensitive generalization of ICA [C]. International Conference on Neural Information Processing, Hong Kong, September 1996, 613-619.
[29] Karhunen J, Joutsensalo J. Representation and separation of signals using nonlinear PCA type learning [J]. Neural Netw., 1994, 7(1): 113-127.
[30] Oja E. The nonlinear PCA learning rule in independent component analysis [J]. Neurocomputing, 1997, 17(1): 25-45.
[31] Cardoso J F, Souloumiac A. Blind beamforming for non Gaussian signals [C]. IEE Proceedings-F, 1993, 140, 362-370.
[32] Hyv?rinen A. New approximations of differential entropy for independent component analysis and projection pursuit [C]. In Proceedings of the 1997 conference on Advances in neural information processing systems, 1998, 273-279.
[33] Amari S, Cichocki A. Adaptive blind signal processing - Neural network approaches [C]. In proceedings of the IEEE, 1998, 86, 2026-2048.
[34] Amari S. Natural gradient works efficiently in learning[J]. Neural Computation, 1998, 10 (2): 251-276.
[35] Squartini S, Piazza F, Shawker A. New Riemannian metrics for improvement of convergence speed in ICA based learning algorithms [J]. ISCAS 2005. IEEE International Symposium on Circuits and Systems, 2005, 4: 3603-3606.
[36] Squartini S, Arcangeli A, Piazza F. Stability analysis of natural gradient learning rules in complete ICA: A unifying perspective [J]. IEEE Signal Processing Letters, 2007, 14 (1): 54-57.
[37] Hyv?rinen A. Survey on Independent Component Analysis [J]. Neural Computing Surveys, 1999, 2: 94-128.
[38] Cardoso J F, Souloumiac A. Jacobi angles for simultaneous diagonalization [J]. SIAM Journal of Matrix Analysis and Applications, 1996, 17(1): 161-164.
[39] Bofill P, Zibulevsky M. Underdetermined blind source separation using sparse representations [J]. Signal Processing, 2001, 81 (11): 2353-2362.
[40] Li Y Q, Amari S, Cichocki A, et al. Underdetermined blind source separation based on sparse representation[J]. IEEE Transactions on Signal Processing,2006, 54(2): 423-437.
[41] Zibulevsky M, Zeevi Y Y, Kisilev P, et al. Blind Source Separation via Multinode Sparse Representation [C]. In Advances in Neural Information Processing Systems, 2001, 1049-1056.
[42] O'Grady P D, Pearlmutter B A. Hard-LOST: Modified k-Means for Oriented Lines [C]. In Proceedings of the Irish Signals and Systems Conference, 2003 247-252.
[43] Li Y Q, Cichocki A, Amari S. Analysis of sparse representation and blind source separation [J]. Neural Computation, 2004, 16(6): 1193-1234.
[44] Zhang W, Liu J, Sun J, et al. A New Two-Stage Approach to Underdetermined Blind Source Separation using Sparse Representation [C]. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, 953-956.
[45] Fang Y, Zhang Y. A robust clustering algorithm for underdetermined blind separation of sparse sources[J], Journal of Shanghai University (English Edition), 2008, 12(3): 228-234.
[46]谭北海,谢胜利.基于源信号数目估计的欠定盲分离[J].电子与信息学报, 2008, 30(4): 863-867.
[47]邱天爽,毕晓辉.稀疏分量分析在欠定盲源分离问题中的研究进展及应用[J].信号处理, 2008, 24(6): 966-970.
[48] Kisilev P, Zibulevsky M, Zeevi Y Y. Blind source separation using multinode sparse representation [C]. In Image Processing, International Conference on, 2001, 3: 202-205.
[49] Theis F J, Lang E W, Puntonet C G.. A geometric algorithm for overcomplete linear ICA [J]. Neurocomputing, 2004, 56(1): 381-398.
[50]张烨,方勇.基于拉普拉斯势函数的欠定盲分离中源数的估计[J].信号处理, 2009, 25(11): 1719-1725.
[51] Lee T W, Lewicki M S, Girolami M, et al. Blind source separation of more sources than mixtures using overcomplete representations [J]. IEEE Signal Processing Letters, 1999, 6(4): 87-90.
[52] Lewicki M S, Sejnowski T J. Learning Overcomplete Representations [J]. Neural Comput., 2000, 12(2): 337-365.
[53]谢胜利,谭北海,傅予力.基于平面聚类算法的欠定混叠盲信号分离[J].自然科学进展, 2007, 17(6): 795-800.
[54] Washizawa Y, Cichocki A. Sparse blind identification and separation by using adaptive K-orthodrome clustering [J]. Neurocomputing, 2008, 71(10-12): 2321-2329.
[55]张烨,方勇.基于平面聚类势函数法的欠定混合信号盲分离[J].高技术通讯, 2010, 20(8): 810-815.
[56] Fevotte C, Godsill S J. A bayesian approach for blind separation of sparse sources[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2006, 14(6): 2174–2188.
[57] Radfar M H, Danserean R M. Single-channel speech separation using soft mask filtering [J]. IEEE Transaction on Audio, Speech and Language Processing, 2007, 15(8):2299-2310.
[58] Zhiyao Duan, Yungang Zhang, Changshui Zhang, et al. Unsupervised single-channel music source separation by average harmonic structure modeling[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2008, 16(4): 766–778.
[59]崔荣涛,李辉,万坚等.一种基于过采样的单通道MPSK信号盲分离算法[J].电子与信息学报, 2009, 31(3): 566-569.
[60]涂世龙,陈越新,郑辉.利用纠错编码的同频调制混合信号单通道盲分离[J].电子与信息学报, 2009, 31(9): 2113-2117.
[61] Yun-Kyung Lee, Oh-Wook Kwon. Application of shape analysis techniques for improved CASA-based speech separation [J]. IEEE Transactions on Consumer Electronics, 2009, 55(1) :146-149.
[62] CAI Quanwei, WEI Ping, XIAO Xianci. Single-channel blind separation of overlapped multicomponents based on energy operator [J]. SCIENCE CHINA-Information Sciences, 2010, 53(1) :147-157.
[63]彭耿,王丰华,黄知涛等.单通道混合信号中周期信号的盲分离[J].湖南大学学报(自然科学版), 2010, 37(4):42-45.
[64]丁志中.盲信号分离及其在心电和语音信号处理中的应用[D].合肥:中国科学技术大学, 2007.
[65] Hopgood J R, Rayner P J W. Single channel nonstationary stochastic signal separation using linear time-varying filters [J]. IEEE Transactions on Signal Processing, 2003,51(7):1 739-1 752.
[66]彭耿,黄知涛,姜文利等.单通道盲信号分离研究进展与展望[J].中国电子科学研究院学报, 2009, 4(3): 268-277.
[67] Atlas L, Janssen C. Coherent modulation spectral filtering for single-channel music source separation[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, 2005, 4: 461-464.
[68] Ozerov A, Philippe P, Gribonval R, et al. One microphone singing voice separation using source-adapted models[C]. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, 2005:90-93.
[69] James C J, Lowe D. Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework[C]. IEEE International Conference on Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001, 1974-1977.
[70] Ping Gao, Ee-Chien Chang, Wyse L. Blind separation of fetal ECG from single mixture using SVD and ICA[C]. International Conference on Information,Communications and Signal Processing, Singapore, 2003, 3:1418-1422.
[71] Warner E S, Proudler I K. Single-channel blind signal separation of filtered MPSK signals [J]. IEE Proceedings of Radar, Sonar and Navigation, 2003, 150(6):396-402.
[72] Lee J, Lee K J, Yoo S K. Development of a new signal processing algorithm based on independent component analysis for single channel ECG data[C]. IEEE International Conference on Engineering in Medicine and Biology Society, San Francisco, CA, USA, 2004, 1: 224-226.
[73] Jang G J, Te-Won Lee, Yung-Hwan Oh. Single-channel signal separation using time-domain basis functions [J]. IEEE Signal Processing Letters, 2003, 10(6): 168–171.
[74] Gil-Jin Jang, Te-Won Lee, Yung-Hwan Oh. Single channel signal separation using MAP-based subspace decomposition [J]. Electronics Letters, 2003, 39(24): 1766–1767.
[75] Reyes-Gomez M J, Ellis D P W, Jojic N. Multiband audio modeling for single-channel acoustic source separation[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Quebec, Canada, 2004, 5: 641-644.
[76] Tuomas Virtanen. Monaural Sound Source Separation by Nonnegative Matrix Factorization with Temporal Continuity and Sparseness Criteria [J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15 (3):1066-1074
[77] Fevotte C, Godsill S J. A bayesian approach for blind separation of sparse sources[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2006, 14(6): 2174–2188.
[78]徐尚志,苏勇,叶中付.欠定条件下的盲分离算法[J].数据采集与处理, 2006, 21(2): 128-132.
[79] Szu H, Chanyagorn P, Kopriva I. Sparse coding blind source separation through powerlinee [J]. Neurocomputing, 2002, 48(1):1015-1020.
[80] Mingyang Wu, DeLiang Wang, Brown G. J. A multipitch tracking algorithm for noisy speech [J]. IEEETransactions on Speech and Audio Processing, 2003, 11(3): 229-241.
[81] Guoning Hu, DeLiang Wang. Monaural speech segregation based on pitch tracking and amplitude modulation [J]. IEEE Transactions on Neural Networks, 2004, 15(5) :1135-1150.
[82] Peng Li, Yong Guan, Bo Xu, et al. Monaural Speech Separation Based onComputational Auditory Scene Analysis and Objective Quality Assessment of Speech [J]. IEEE Transactions onAudio, Speech, and Language Processing, 2006, 14(6): 2014-2023.
[83] Radfar M H, Danserean R M, Sayadiyan A. A maximum likelihood estimation of vocal-tract-related filter characteristics for single channel speech separation [J]. EURASIP Journal on Audio,Speech,and Music Processing, 2007, (1): 1-15.
[84]张晓冬,王桥,吴乐南.利用脊的特征进行信号盲分离[J].电子学报, 2004, 32(7): 1156-1159.
[85]蔡权伟,魏平,肖先赐.基于模型拟合的重叠信号盲分离方法[J].电子学报, 2005, 33(10): 1794-1798.
[86] Vincent E, Gribonval R, Fevotte C. Performance measurement in blind audio source separation [J]. IEEE Transaction on Audio, Speech, and Language Processing, 2006, 14(4): 1462–1469.
[87]黄青华.基于源信号模型的盲分离技术研究及应用[D].上海:上海交通大学, 2007.
[88]刘凯.粒子滤波在单通道信号分离中的应用研究[D].合肥:中国科学技术大学, 2007.
[89]方勇,王伟,王睿.置换混叠信号的盲检测和分离[J].应用科学学报,2009,27(5):491-497.
[90]王伟,方勇.基于有限差分的置换图像盲检测方法[J].电子学报,2010, 38(10):2268-2272.
[91] Wei Wang, Yong Fang. Single-Channel Blind Separation of JPEG Permuted Image Using Double Compression. The 2nd IEEE/IET International Conference on Audio, Language and Image Processing, Shanghai, China. 2010:439-443.
[92]王伟,方勇.基于二次模糊相关性的单通道置换图像盲分离[J].应用科学学报,2011,29(2):169-175.
[93] FANG Yong, WANG Wei. Blind-restoration-based blind separation method for permuted motion blurred images[J], Journal of Shanghai University (English Edition), 2011, 15 (2): 79-84.
[94] Rafael C. Gonzalez. Digital Image Processing Using MATLAB [M]. Beijin: Publishing House of Electronics Industry, 2005. [冈萨雷斯.数字图像处理(Matlab版) [M].北京:电子工业出版社,2005].
[95]戴蒙,林家骏,毛家发. JPEG二次压缩的分析与检测[J].中国图象图形学报,2006,11(11): 1619-1622.
[96] Zhigang Fan, de Queiroz, R.L. Identification of bitmap compression history: JPEG detection and quantizer Estimation [J]. IEEE Transaction on Image Processing, 2003,12(2):230-235.
[97] Pevny T, Fridrich J. Detection of double-compression in JPEG image for applications in steganography. IEEE Transactions on Information Forensics and Security, 2008, 3(2): 247-258.
[98]李晟,张新鹏.利用JPEG压缩特性的合成图像检测[J].应用科学学报,2008,26(3):281-287.
[99] Farid H. Exposing Digital Forgeries From JPEG Ghosts. IEEE Transactions on Information Forensics and Security, 2009, 4(1): 154-160.
[100]郑二功,平西建.针对一类JPEG图像伪造的被动盲取证[J].电子与信息学报, 2010, 32(2): 394-399.
[101]周琳娜.数字图像盲取证技术研究[D].北京:北京邮电大学,2007
[102] Farid H. Creating and detecting doctored and virtual images: Implications to the child pornography prevention act. http://www.ists.dartmouth.edu/library/ tr-2004-518.pdf. 2008,2.
[103] Lukas J, Fridrich J. Estimation of primary quantization matrix in double compressed JPEG images[C]. Digital Forensic Research Workshop Proceedings, Cleveland, OH, USA, Aug.6-8, 2003:67-84.
[104]王俊文,刘光杰,戴跃伟等.一种估计JPEG双重压缩原始量化步长的新方法[J].电子与信息学报,2009,31(4):836-839.
[105] Luo Wei-qi, Qu Zhen-hua, Huang Ji-wu, et al. A novel method for detecting cropped and recompressed image block[C]. International Conference on Acoustics, Speech and Signal Processing Proceedings, Hawaii, USA, 2007:217-220.
[106] He J F, Lin Z C, Wang L F, et al. Detecting doctored JPEG images via DCT coefficient analysis [C]. 9th European Conference on Computer Vision Proceedings, Graz, Austria, 2006:423-435.
[107] Li Wei-hai, Yu Neng-hai, Yuan Yuan. Doctored JPEG image detection [C]. IEEE International Conference on Multimedia and Expo Proceedings, Hannover, Germany, 2008:253-256.
[108] Ye Shui-ming, Sun Qi-bin, Chang Ee-chien. Detecting digital image forgeries by measuring inconsistencies of blocking artifact[C]. IEEE International Conference on Multimedia and Expo Proceedings, Beijing, China, 2007:12-15.
[109] Anarim E, Ucar H, Istefanopulos Y, Identification of image and blur parameters in frequency domain using the EM algorithm [J]. IEEE Transactions on Image Processing, 1996, 5(1): 159-164.
[110]邹谋炎.盲反卷积和信号复原[M].第一版,北京,国防工业出版社, 2001:189-194.
[111]邓泽峰,熊有伦.基于频域方法的运动模糊方向识别[J].光电工程,2007,34(10):98-101.
[112] Gonzalez R C, Woods R E, Eddins S L. Digital Image Processing Using Matlab[M], USA: Pearson Prentice Hall, 2004.
[113]周曲,颜国正,王文兴.相关系数分析在模糊图像参数识别中的应用[J].光学精密工程,2007,15(6):988-995.
[114] ZHOU Qu, YAN Guo-zheng, WANG Wen-xing. Parameter Estimation for Blur Image Combining Defocus and Motion Blur using Cepstrum Analysis [J]. Journal of Shanghai Jiaotong University (Science), 2007, E-12(6):700-706
[115]加春燕,崔丽,黄海洋. Gabor变换估计运动模糊方向角[J].北京师范大学学报(自然科学版), 2008,44(6):563-567.
[116]许元男,赵远,刘丽萍等.基于双谱的点扩展函数参数辨识[J].光电工程,2009,36(5):72-76.
[117]许元男,赵远,刘丽萍等.含噪声模糊图像的点扩展函数参数辨识[J].光学精密工程,2009,17(11):2849-2856.
[118]蒋岩峰,于起峰,梁永辉.基于频谱分析的匀速运动模糊图像模糊方向识别[J].光学与光电技术,2008,6(4):68-70.
[119]许元男,赵远,刘丽萍等.基于局部运动模糊图像的测速方法[J].光电工程,2009,36(10):71-80.
[120] Xin Wang, Bo Xuan, Si-long Peng. Digital image forgery detection based on the consistency of defocus blur. In: Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, HarBin, China, 2008:192-195.
[121]何超,方勇.基于公共因子提取的数字图像篡改检测[J].系统仿真技术,2011,7(1):6-10.
[122] Parker J A, Kenyon R V, Troxel D E. Comparison of interpolating methods for Image resampling [J], IEEE Transaction on Medical Imaging, 1983, 2(1):31-39.
[123] Gallagher A C. Detection of linear and cubic interpolation in JPEG compressed images [A]. In Proc. IEEE CRV’05[C]. Canada, 2005:65-72
[124] Mahdian B, Saic S. Blind authentication using periodic properties of interpolation [J]. IEEE Transactions on Information Forensics and Security, 2008, 3(3):529-538.
[125] Popescu A C, Farid H. Exposing digital forgeries by detecting traces of resampling [J], IEEE Transactions on Signal Processing, 2005, 53(2):758-767.
[126] Kirchner M, Bohme R. Hiding traces of resampling in digital image[J], IEEE Transaction on Information Forensics and Security, 2008, 3(4):582-592.
[127] Hsiao D Y, Pei S C. Detection digital tampering by blur estimation. In: Proceeding of the First International Workshop on Approaches to Digital Forensic Engineering, Taipei, Taiwan, 2005: 264-278.
[128]王波,孙璐璐,孔祥维等.图像伪造中模糊操作的异常色调率取证技术[J].电子学报,2006,34(12A):2451-2454.
[129] Sutcu Y, Coskun B, Sencar H T, et al. Tamper detection based on regularity of wavelet transform coefficients. In: Proceedings of IEEE International Conference on Image Processing, San Antonio, TX, USA, 2007:397-400.
[130]周琳娜,王东明,郭云彪等.基于数字图像边缘特性的形态学滤波取证技术[J].电子学报,2008,36(6):1047-1051.
[131]王睿,方勇.一种图像局部模糊检测方法及在被动图像认证中的应用[J].高技术通讯,2009,19(7):718-723.
[2]张烨.欠定混合信号的盲分离[D].上海:上海大学, 2009.
[3]杨祖元.盲信号分离算法分析及应用研究[D].广州:华南理工大学, 2009.
[4]周郭许.盲信号分离若干关键问题研究[D].广州:华南理工大学, 2010.
[5] Casanovas A L, Monaci G, Vandergheynst P. Blind audiovisual source separation using sparse representations [C]. Presented at IEEE International Conference on Image Processing, 2007.
[6] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries [J]. IEEE Trans. Image Processing, 2006, 15(12): 3736-3745.
[7] Bobin J, Starck J L, Fadili J, et al. Sparsity and morphological diversity in blind source separation [J]. IEEE Trans. Image Processing, 2007, 16(11): 2662-2674.
[8] Makeig S, Jung T P, Bell A J, et al. Blind separation of auditory event-related brain responses into independent components [J]. Proceedings of the National Academy of Sciences of the United States of America, 1997, 94(20): 10979-10984.
[9] Mitianoudis N, Davies M E. Audio source separation of convolutive mixtures [J]. IEEE Trans. Speech and Audio Processing, 2003, 11(5): 489-497.
[10] Lin Q.H., Yin F.L., Mei T.M., et al. A blind source separation based method for speech encryption [J]. IEEE Trans. Circuits and System I, 2006, 53(6): 1320-1328.
[11] Li S, Li C, Lo K.-T, et al. Cryptanalyzing an encryption scheme based on blind source separation [J]. IEEE Trans. Circuits and System I, 2008, 55(4): 1055-1063.
[12] Talwar S, Paulraj A. Blind separation of synchronous co-channel digital signals using an antenna array .2. Performance analysis [J]. IEEE Transactions on Signal Processing, 1997, 45(3): 706-718.
[13] Sahlin H. and Broman H. Separation of real-world signals [J]. Signal Processing, 1998, 64(2): 103-113.
[14] Ristaniemi T. and Joutsensalo Y. Independent component analysis with code information utilization in DS-CDMA signal separation [C]. Presented at Seamless Interconnection for Universal Services, 1999.
[15] Cichocki A, Amari S. Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications [M]. Wiley, 2003.
[16] Flexer A,Bauer H,Pripfl J,et al. Using ICA for removal of ocular artifacts in EEG recorded from blind subjects [J]. Neural Networks, 2005, 18(7):998-1005.
[17] Moosmann M, Eiehele T, Nordby H, et al. Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation. Inernational Journal of Psyehophysiology. 2008,67(3): 212-221.
[18] Common P. Independent component analysis, A new concept? [J]. Signal Processing, Elsevier, 1994, 36 (3): 287-314.
[19] Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis [M]. New York: Wiley, 2001.
[20] Bell A, Sejnowski T. An information-maximization approach to blind separation and blind deconvolution [J]. Neural Computation, 1995, 7 (6): 1129-1159.
[21] Cardoso J F, Laheld B H. Equivariant adaptive source separation [J]. IEEE Transactions on Signal Processing, 1996, 44 (12): 3017-3030.
[22] Belouchrani A, AbedMeraim K, Cardoso J F, et al. A blind source separation technique using second-order statistics [J]. IEEE Transactions on Signal Processing, 1997, 45 (2): 434-444.
[23] Hyvarinen A, Oja E. A fast fixed-point algorithm for independent component analysis [J]. Neural Computation, 1997, 9 (7): 1483-1492.
[24] Cardoso J F. Blind signal separation: Statistical principles [J]. Proceedings of the IEEE, 1998, 86 (10): 2009-2025.
[25] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. IEEE Transactions on Neural Networks, 1999, 10 (3): 626-634.
[26] Hyvarinen A. "The FastICA Matlab package. V2.5," 2005.
[27] Cardoso J F. Infomax and maximum likelihood for blind source separation [J]. IEEE Signal Processing Letters, 1997, 4 (4): 112-114.
[28] Pearlmutter B, Parra L. A context-sensitive generalization of ICA [C]. International Conference on Neural Information Processing, Hong Kong, September 1996, 613-619.
[29] Karhunen J, Joutsensalo J. Representation and separation of signals using nonlinear PCA type learning [J]. Neural Netw., 1994, 7(1): 113-127.
[30] Oja E. The nonlinear PCA learning rule in independent component analysis [J]. Neurocomputing, 1997, 17(1): 25-45.
[31] Cardoso J F, Souloumiac A. Blind beamforming for non Gaussian signals [C]. IEE Proceedings-F, 1993, 140, 362-370.
[32] Hyv?rinen A. New approximations of differential entropy for independent component analysis and projection pursuit [C]. In Proceedings of the 1997 conference on Advances in neural information processing systems, 1998, 273-279.
[33] Amari S, Cichocki A. Adaptive blind signal processing - Neural network approaches [C]. In proceedings of the IEEE, 1998, 86, 2026-2048.
[34] Amari S. Natural gradient works efficiently in learning[J]. Neural Computation, 1998, 10 (2): 251-276.
[35] Squartini S, Piazza F, Shawker A. New Riemannian metrics for improvement of convergence speed in ICA based learning algorithms [J]. ISCAS 2005. IEEE International Symposium on Circuits and Systems, 2005, 4: 3603-3606.
[36] Squartini S, Arcangeli A, Piazza F. Stability analysis of natural gradient learning rules in complete ICA: A unifying perspective [J]. IEEE Signal Processing Letters, 2007, 14 (1): 54-57.
[37] Hyv?rinen A. Survey on Independent Component Analysis [J]. Neural Computing Surveys, 1999, 2: 94-128.
[38] Cardoso J F, Souloumiac A. Jacobi angles for simultaneous diagonalization [J]. SIAM Journal of Matrix Analysis and Applications, 1996, 17(1): 161-164.
[39] Bofill P, Zibulevsky M. Underdetermined blind source separation using sparse representations [J]. Signal Processing, 2001, 81 (11): 2353-2362.
[40] Li Y Q, Amari S, Cichocki A, et al. Underdetermined blind source separation based on sparse representation[J]. IEEE Transactions on Signal Processing,2006, 54(2): 423-437.
[41] Zibulevsky M, Zeevi Y Y, Kisilev P, et al. Blind Source Separation via Multinode Sparse Representation [C]. In Advances in Neural Information Processing Systems, 2001, 1049-1056.
[42] O'Grady P D, Pearlmutter B A. Hard-LOST: Modified k-Means for Oriented Lines [C]. In Proceedings of the Irish Signals and Systems Conference, 2003 247-252.
[43] Li Y Q, Cichocki A, Amari S. Analysis of sparse representation and blind source separation [J]. Neural Computation, 2004, 16(6): 1193-1234.
[44] Zhang W, Liu J, Sun J, et al. A New Two-Stage Approach to Underdetermined Blind Source Separation using Sparse Representation [C]. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, 953-956.
[45] Fang Y, Zhang Y. A robust clustering algorithm for underdetermined blind separation of sparse sources[J], Journal of Shanghai University (English Edition), 2008, 12(3): 228-234.
[46]谭北海,谢胜利.基于源信号数目估计的欠定盲分离[J].电子与信息学报, 2008, 30(4): 863-867.
[47]邱天爽,毕晓辉.稀疏分量分析在欠定盲源分离问题中的研究进展及应用[J].信号处理, 2008, 24(6): 966-970.
[48] Kisilev P, Zibulevsky M, Zeevi Y Y. Blind source separation using multinode sparse representation [C]. In Image Processing, International Conference on, 2001, 3: 202-205.
[49] Theis F J, Lang E W, Puntonet C G.. A geometric algorithm for overcomplete linear ICA [J]. Neurocomputing, 2004, 56(1): 381-398.
[50]张烨,方勇.基于拉普拉斯势函数的欠定盲分离中源数的估计[J].信号处理, 2009, 25(11): 1719-1725.
[51] Lee T W, Lewicki M S, Girolami M, et al. Blind source separation of more sources than mixtures using overcomplete representations [J]. IEEE Signal Processing Letters, 1999, 6(4): 87-90.
[52] Lewicki M S, Sejnowski T J. Learning Overcomplete Representations [J]. Neural Comput., 2000, 12(2): 337-365.
[53]谢胜利,谭北海,傅予力.基于平面聚类算法的欠定混叠盲信号分离[J].自然科学进展, 2007, 17(6): 795-800.
[54] Washizawa Y, Cichocki A. Sparse blind identification and separation by using adaptive K-orthodrome clustering [J]. Neurocomputing, 2008, 71(10-12): 2321-2329.
[55]张烨,方勇.基于平面聚类势函数法的欠定混合信号盲分离[J].高技术通讯, 2010, 20(8): 810-815.
[56] Fevotte C, Godsill S J. A bayesian approach for blind separation of sparse sources[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2006, 14(6): 2174–2188.
[57] Radfar M H, Danserean R M. Single-channel speech separation using soft mask filtering [J]. IEEE Transaction on Audio, Speech and Language Processing, 2007, 15(8):2299-2310.
[58] Zhiyao Duan, Yungang Zhang, Changshui Zhang, et al. Unsupervised single-channel music source separation by average harmonic structure modeling[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2008, 16(4): 766–778.
[59]崔荣涛,李辉,万坚等.一种基于过采样的单通道MPSK信号盲分离算法[J].电子与信息学报, 2009, 31(3): 566-569.
[60]涂世龙,陈越新,郑辉.利用纠错编码的同频调制混合信号单通道盲分离[J].电子与信息学报, 2009, 31(9): 2113-2117.
[61] Yun-Kyung Lee, Oh-Wook Kwon. Application of shape analysis techniques for improved CASA-based speech separation [J]. IEEE Transactions on Consumer Electronics, 2009, 55(1) :146-149.
[62] CAI Quanwei, WEI Ping, XIAO Xianci. Single-channel blind separation of overlapped multicomponents based on energy operator [J]. SCIENCE CHINA-Information Sciences, 2010, 53(1) :147-157.
[63]彭耿,王丰华,黄知涛等.单通道混合信号中周期信号的盲分离[J].湖南大学学报(自然科学版), 2010, 37(4):42-45.
[64]丁志中.盲信号分离及其在心电和语音信号处理中的应用[D].合肥:中国科学技术大学, 2007.
[65] Hopgood J R, Rayner P J W. Single channel nonstationary stochastic signal separation using linear time-varying filters [J]. IEEE Transactions on Signal Processing, 2003,51(7):1 739-1 752.
[66]彭耿,黄知涛,姜文利等.单通道盲信号分离研究进展与展望[J].中国电子科学研究院学报, 2009, 4(3): 268-277.
[67] Atlas L, Janssen C. Coherent modulation spectral filtering for single-channel music source separation[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, 2005, 4: 461-464.
[68] Ozerov A, Philippe P, Gribonval R, et al. One microphone singing voice separation using source-adapted models[C]. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, 2005:90-93.
[69] James C J, Lowe D. Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework[C]. IEEE International Conference on Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001, 1974-1977.
[70] Ping Gao, Ee-Chien Chang, Wyse L. Blind separation of fetal ECG from single mixture using SVD and ICA[C]. International Conference on Information,Communications and Signal Processing, Singapore, 2003, 3:1418-1422.
[71] Warner E S, Proudler I K. Single-channel blind signal separation of filtered MPSK signals [J]. IEE Proceedings of Radar, Sonar and Navigation, 2003, 150(6):396-402.
[72] Lee J, Lee K J, Yoo S K. Development of a new signal processing algorithm based on independent component analysis for single channel ECG data[C]. IEEE International Conference on Engineering in Medicine and Biology Society, San Francisco, CA, USA, 2004, 1: 224-226.
[73] Jang G J, Te-Won Lee, Yung-Hwan Oh. Single-channel signal separation using time-domain basis functions [J]. IEEE Signal Processing Letters, 2003, 10(6): 168–171.
[74] Gil-Jin Jang, Te-Won Lee, Yung-Hwan Oh. Single channel signal separation using MAP-based subspace decomposition [J]. Electronics Letters, 2003, 39(24): 1766–1767.
[75] Reyes-Gomez M J, Ellis D P W, Jojic N. Multiband audio modeling for single-channel acoustic source separation[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Quebec, Canada, 2004, 5: 641-644.
[76] Tuomas Virtanen. Monaural Sound Source Separation by Nonnegative Matrix Factorization with Temporal Continuity and Sparseness Criteria [J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15 (3):1066-1074
[77] Fevotte C, Godsill S J. A bayesian approach for blind separation of sparse sources[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2006, 14(6): 2174–2188.
[78]徐尚志,苏勇,叶中付.欠定条件下的盲分离算法[J].数据采集与处理, 2006, 21(2): 128-132.
[79] Szu H, Chanyagorn P, Kopriva I. Sparse coding blind source separation through powerlinee [J]. Neurocomputing, 2002, 48(1):1015-1020.
[80] Mingyang Wu, DeLiang Wang, Brown G. J. A multipitch tracking algorithm for noisy speech [J]. IEEETransactions on Speech and Audio Processing, 2003, 11(3): 229-241.
[81] Guoning Hu, DeLiang Wang. Monaural speech segregation based on pitch tracking and amplitude modulation [J]. IEEE Transactions on Neural Networks, 2004, 15(5) :1135-1150.
[82] Peng Li, Yong Guan, Bo Xu, et al. Monaural Speech Separation Based onComputational Auditory Scene Analysis and Objective Quality Assessment of Speech [J]. IEEE Transactions onAudio, Speech, and Language Processing, 2006, 14(6): 2014-2023.
[83] Radfar M H, Danserean R M, Sayadiyan A. A maximum likelihood estimation of vocal-tract-related filter characteristics for single channel speech separation [J]. EURASIP Journal on Audio,Speech,and Music Processing, 2007, (1): 1-15.
[84]张晓冬,王桥,吴乐南.利用脊的特征进行信号盲分离[J].电子学报, 2004, 32(7): 1156-1159.
[85]蔡权伟,魏平,肖先赐.基于模型拟合的重叠信号盲分离方法[J].电子学报, 2005, 33(10): 1794-1798.
[86] Vincent E, Gribonval R, Fevotte C. Performance measurement in blind audio source separation [J]. IEEE Transaction on Audio, Speech, and Language Processing, 2006, 14(4): 1462–1469.
[87]黄青华.基于源信号模型的盲分离技术研究及应用[D].上海:上海交通大学, 2007.
[88]刘凯.粒子滤波在单通道信号分离中的应用研究[D].合肥:中国科学技术大学, 2007.
[89]方勇,王伟,王睿.置换混叠信号的盲检测和分离[J].应用科学学报,2009,27(5):491-497.
[90]王伟,方勇.基于有限差分的置换图像盲检测方法[J].电子学报,2010, 38(10):2268-2272.
[91] Wei Wang, Yong Fang. Single-Channel Blind Separation of JPEG Permuted Image Using Double Compression. The 2nd IEEE/IET International Conference on Audio, Language and Image Processing, Shanghai, China. 2010:439-443.
[92]王伟,方勇.基于二次模糊相关性的单通道置换图像盲分离[J].应用科学学报,2011,29(2):169-175.
[93] FANG Yong, WANG Wei. Blind-restoration-based blind separation method for permuted motion blurred images[J], Journal of Shanghai University (English Edition), 2011, 15 (2): 79-84.
[94] Rafael C. Gonzalez. Digital Image Processing Using MATLAB [M]. Beijin: Publishing House of Electronics Industry, 2005. [冈萨雷斯.数字图像处理(Matlab版) [M].北京:电子工业出版社,2005].
[95]戴蒙,林家骏,毛家发. JPEG二次压缩的分析与检测[J].中国图象图形学报,2006,11(11): 1619-1622.
[96] Zhigang Fan, de Queiroz, R.L. Identification of bitmap compression history: JPEG detection and quantizer Estimation [J]. IEEE Transaction on Image Processing, 2003,12(2):230-235.
[97] Pevny T, Fridrich J. Detection of double-compression in JPEG image for applications in steganography. IEEE Transactions on Information Forensics and Security, 2008, 3(2): 247-258.
[98]李晟,张新鹏.利用JPEG压缩特性的合成图像检测[J].应用科学学报,2008,26(3):281-287.
[99] Farid H. Exposing Digital Forgeries From JPEG Ghosts. IEEE Transactions on Information Forensics and Security, 2009, 4(1): 154-160.
[100]郑二功,平西建.针对一类JPEG图像伪造的被动盲取证[J].电子与信息学报, 2010, 32(2): 394-399.
[101]周琳娜.数字图像盲取证技术研究[D].北京:北京邮电大学,2007
[102] Farid H. Creating and detecting doctored and virtual images: Implications to the child pornography prevention act. http://www.ists.dartmouth.edu/library/ tr-2004-518.pdf. 2008,2.
[103] Lukas J, Fridrich J. Estimation of primary quantization matrix in double compressed JPEG images[C]. Digital Forensic Research Workshop Proceedings, Cleveland, OH, USA, Aug.6-8, 2003:67-84.
[104]王俊文,刘光杰,戴跃伟等.一种估计JPEG双重压缩原始量化步长的新方法[J].电子与信息学报,2009,31(4):836-839.
[105] Luo Wei-qi, Qu Zhen-hua, Huang Ji-wu, et al. A novel method for detecting cropped and recompressed image block[C]. International Conference on Acoustics, Speech and Signal Processing Proceedings, Hawaii, USA, 2007:217-220.
[106] He J F, Lin Z C, Wang L F, et al. Detecting doctored JPEG images via DCT coefficient analysis [C]. 9th European Conference on Computer Vision Proceedings, Graz, Austria, 2006:423-435.
[107] Li Wei-hai, Yu Neng-hai, Yuan Yuan. Doctored JPEG image detection [C]. IEEE International Conference on Multimedia and Expo Proceedings, Hannover, Germany, 2008:253-256.
[108] Ye Shui-ming, Sun Qi-bin, Chang Ee-chien. Detecting digital image forgeries by measuring inconsistencies of blocking artifact[C]. IEEE International Conference on Multimedia and Expo Proceedings, Beijing, China, 2007:12-15.
[109] Anarim E, Ucar H, Istefanopulos Y, Identification of image and blur parameters in frequency domain using the EM algorithm [J]. IEEE Transactions on Image Processing, 1996, 5(1): 159-164.
[110]邹谋炎.盲反卷积和信号复原[M].第一版,北京,国防工业出版社, 2001:189-194.
[111]邓泽峰,熊有伦.基于频域方法的运动模糊方向识别[J].光电工程,2007,34(10):98-101.
[112] Gonzalez R C, Woods R E, Eddins S L. Digital Image Processing Using Matlab[M], USA: Pearson Prentice Hall, 2004.
[113]周曲,颜国正,王文兴.相关系数分析在模糊图像参数识别中的应用[J].光学精密工程,2007,15(6):988-995.
[114] ZHOU Qu, YAN Guo-zheng, WANG Wen-xing. Parameter Estimation for Blur Image Combining Defocus and Motion Blur using Cepstrum Analysis [J]. Journal of Shanghai Jiaotong University (Science), 2007, E-12(6):700-706
[115]加春燕,崔丽,黄海洋. Gabor变换估计运动模糊方向角[J].北京师范大学学报(自然科学版), 2008,44(6):563-567.
[116]许元男,赵远,刘丽萍等.基于双谱的点扩展函数参数辨识[J].光电工程,2009,36(5):72-76.
[117]许元男,赵远,刘丽萍等.含噪声模糊图像的点扩展函数参数辨识[J].光学精密工程,2009,17(11):2849-2856.
[118]蒋岩峰,于起峰,梁永辉.基于频谱分析的匀速运动模糊图像模糊方向识别[J].光学与光电技术,2008,6(4):68-70.
[119]许元男,赵远,刘丽萍等.基于局部运动模糊图像的测速方法[J].光电工程,2009,36(10):71-80.
[120] Xin Wang, Bo Xuan, Si-long Peng. Digital image forgery detection based on the consistency of defocus blur. In: Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, HarBin, China, 2008:192-195.
[121]何超,方勇.基于公共因子提取的数字图像篡改检测[J].系统仿真技术,2011,7(1):6-10.
[122] Parker J A, Kenyon R V, Troxel D E. Comparison of interpolating methods for Image resampling [J], IEEE Transaction on Medical Imaging, 1983, 2(1):31-39.
[123] Gallagher A C. Detection of linear and cubic interpolation in JPEG compressed images [A]. In Proc. IEEE CRV’05[C]. Canada, 2005:65-72
[124] Mahdian B, Saic S. Blind authentication using periodic properties of interpolation [J]. IEEE Transactions on Information Forensics and Security, 2008, 3(3):529-538.
[125] Popescu A C, Farid H. Exposing digital forgeries by detecting traces of resampling [J], IEEE Transactions on Signal Processing, 2005, 53(2):758-767.
[126] Kirchner M, Bohme R. Hiding traces of resampling in digital image[J], IEEE Transaction on Information Forensics and Security, 2008, 3(4):582-592.
[127] Hsiao D Y, Pei S C. Detection digital tampering by blur estimation. In: Proceeding of the First International Workshop on Approaches to Digital Forensic Engineering, Taipei, Taiwan, 2005: 264-278.
[128]王波,孙璐璐,孔祥维等.图像伪造中模糊操作的异常色调率取证技术[J].电子学报,2006,34(12A):2451-2454.
[129] Sutcu Y, Coskun B, Sencar H T, et al. Tamper detection based on regularity of wavelet transform coefficients. In: Proceedings of IEEE International Conference on Image Processing, San Antonio, TX, USA, 2007:397-400.
[130]周琳娜,王东明,郭云彪等.基于数字图像边缘特性的形态学滤波取证技术[J].电子学报,2008,36(6):1047-1051.
[131]王睿,方勇.一种图像局部模糊检测方法及在被动图像认证中的应用[J].高技术通讯,2009,19(7):718-723.