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系统阐述了利用稀疏成分分析(Sparse Component Analysis,SCA)算法进行欠定图像盲源分离。首先在估计出源图像个数的基础上,利用线性聚类估计混合矩阵;其次将压缩感知(Compressed Sensing,CS)应用到恢复源图像中。为了得到自适应的过完备稀疏字典来提高分离效果,提出了利用K均值奇异值分解(K-means Singular Value Decomposition,K-SVD)算法对过完备DCT字典循环迭代训练的思想,并对图像分块处理来减少计算复杂度;最后进行了仿真测试并对分离出的图像进行了分析和进一步处理。 相似文献
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This work considers a regularized Fincke‐Pohst Sphere Decoder (R‐FSD) to detect the multiuser data of orthogonal frequency division multiplexing/space division multiple access (OFDM/SDMA) uplink system with underdetermined and rank‐deficient channel. Rank‐deficiency of the detected channel coefficients makes error‐free multiuser detection (MUD) a difficult task. In literature, most of the papers deal with either a determined or over‐determined full‐rank system. The method proposed in this work transforms an original ill‐posed least squares (LS) problem to a well‐posed one at the receiver, by using the standard Tikhonov regularization method. This is an efficient, direct, and less complex approach where the channel is modified using a regularization parameter that adapts to the signal power at the receiver. The result obtained is compared with maximum likelihood (ML), zero forcing (ZF), minimum mean squared error (MMSE) and ordered successive interference cancellation (OSIC) based detection techniques. 相似文献
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Most existing algorithms for the underdetermined blind source separation (UBSS) problem are two-stage algorithm, i.e., mixing parameters estimation and sources estimation. In the mixing parameters estimation, the previously proposed traditional clustering algorithms are sensitive to the initializations of the mixing parameters. To reduce the sensitiveness to the initialization, we propose a new algorithm for the UBSS problem based on anechoic speech mixtures by employing the visual information, i.e., the interaural time difference (ITD) and the interaural level difference (ILD), as the initializations of the mixing parameters. In our algorithm, the video signals are utilized to estimate the distances between microphones and sources, and then the estimations of the ITD and ILD can be obtained. With the sparsity assumption in the time-frequency domain, the Gaussian potential function algorithm is utilized to estimate the mixing parameters by using the ITDs and ILDs as the initializations of the mixing parameters. And the time-frequency masking is used to recover the sources by evaluating the various ITDs and ILDs. Experimental results demonstrate the competitive performance of the proposed algorithm compared with the baseline algorithms. 相似文献
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Ivica Kopriva Ivanka Jeri Marko Filipovi Lidija Brklja
i 《Journal of Chemometrics》2014,28(9):704-715
Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of nonlinear multicomponent mixtures. This paper presents a method for nonlinear underdetermined blind separation of nonnegative dependent sources that comply with a sparse probabilistic model, that is, sources are constrained to be sparse in support and amplitude. This model is validated on experimental pure component mass spectra. Under a sparse prior, a nonlinear problem is converted into an equivalent linear one comprised of original sources and their higher‐order, mostly second‐order, monomials. The influence of these monomials, which stand for error terms, is reduced by preprocessing a matrix of mixtures by means of robust principal component analysis and hard, soft and trimmed thresholding. Preprocessed data matrices are mapped in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions by means of an empirical kernel map. Sparseness‐constrained nonnegative matrix factorizations in RKHS yield sets of separated components. They are assigned to pure components from the library using a maximal correlation criterion. The methodology is exemplified on demanding numerical and experimental examples related respectively to extraction of eight dependent components from three nonlinear mixtures and to extraction of 25 dependent analytes from nine nonlinear mixture mass spectra recorded in nonlinear chemical reaction of peptide synthesis. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents a method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixture‐wise mapping of observed data in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness‐constrained nonnegative matrix factorization (NMF) therein. Thus, the original problem is converted into new one with increased number of mixtures, increased number of dependent sources, and higher‐order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, which is the case for mass spectra of analytes, sparseness‐constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on the original problem. The method is exemplified on numerical and experimental examples related respectively to extraction of 10 dependent components from five mixtures and to extraction of 10 dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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