共查询到10条相似文献,搜索用时 90 毫秒
1.
2.
利用独立分量分析(ICA)的自适应粒子群(APSO)算法对因传输等过程而引起的多幅灰度图像混叠进行盲分离,针对图像盲分离提出了一种基于改进的APSO的盲源分离算法并将其应用于分离模糊灰度图像。利用峰度和负熵分别作为粒子群算法的第一和第二适应度函数根据其高斯性原理作为独立性判别标准对分离矩阵进行自适应更新。分析比较不同盲分离算法对图像分离的收敛性,仿真结果证明改进的自适应粒子群算法能够很好地分离图像且计算性能指标优越,收敛效果好。 相似文献
3.
独立分量分析(ICA)作为一种有效的盲源分离技术(BSS)是信号处理领域的热点。传统的独立分量分析都要求观察信号数目大于或者等于源信号数目,然而对于脑电图(EEG)等的一些信号处理中存在的源信号数目大于观察信号数目的情况,传统的独立分量分析算法不能有效分离。该文针对源信号数目大于观察信号数目的情况,在传统的独立分量分析技术的基础上,给出了一个新的学习算法,并将新算法与传统的独立分量算法进行了比较。实验仿真结果证明该算法在给定2个混合信号的情况下能够较好地分离3个未知语音信号源,成功实现了源信号数目大于观察信号数目情况下的盲源分离。 相似文献
4.
Byeong Il Lee Jae Sung Lee Dong Soo Lee Won Jun Kang Jong Jin Lee Seungjin Choi 《Journal of Signal Processing Systems》2007,49(2):233-241
Ensemble independent component analysis (ICA) is a Bayesian multivariate data analysis method which allows various prior distributions for parameters and latent variables, leading to flexible data fitting. In this paper we apply ensemble ICA with a rectified Gaussian prior to dynamic \( H^{{15}}_{2} O \) positron emission tomography (PET) image data, emphasizing its clinical usefulness by showing that major cardiac components are successfully extracted in an unsupervised manner and myocardial blood flow can be estimated in 15 among 20 patients. Detailed experiments and results are illustrated. 相似文献
5.
This paper presents a novel maximum a posteriori (MAP) denoising algorithm based on the independent component analysis (ICA). We demonstrate that the employment of individual ICA transformations for signal and noise can provide the best estimate within the linear framework. The signal enhancement problem is categorized based on the distribution of signal and noise being Gaussian or non-Gaussian and the estimation rule is derived for each of the categories. Our theoretical analysis shows that under the assumption of a Gaussian noise the proposed algorithm leads to some well-known enhancement techniques, i.e., Wiener filter and sparse code shrinkage. The analysis of the denoising capability shows that the proposed algorithm is most efficient for non-Gaussian signals corrupted by a non-Gaussian noise. We employed the generalized Gaussian model (GGM) to model the distributions of speech and noise. Experimental evaluation is performed in terms of signal-to-noise ratio (SNR) and spectral distortion measure. Experimental results show that the proposed algorithms achieve significant improvement on the enhancement performance in both Gaussian and non-Gaussian noise. 相似文献
6.
Blind source separation (BSS) has an extensive application prospect in many fields, and independent component analysis (ICA) is a very effective tool for solving the BSS problem. Noisy BSS/ICA, as it approaches the reality, is frequently considered in many practical applications. In this paper, we mainly discuss the “sensor” noise, adding Gaussian white noise to the music audio mixtures. To solve noisy BSS/ICA problem, we deploy denoising pre-processing before performing FastICA. Rather than traditional wavelet shrinkage, we employ a more advanced shrinkage denoising algorithm, parallel coordinate descent (PCD) iterative shrinkage based on redundant dictionary, to accomplish the denoising task. Since the classical nonlinearities (tanh and gauss) used in FastICA are not the optimal ones due to their slow computational speed, we propose two novel rational nonlinearities that have faster computational speed and almost the same or better separation performance comparing with the classical ones. As they originate from Pade approximant of tanh and gauss, but the coefficients are adjusted, we name them Variant Tanh Pade (VTP) and Variant Gauss Pade (VGP), respectively. 相似文献
7.
8.
Independent component analysis (ICA) is the primary statistical method for solving the problems of blind source separation.The fast ICA is a famous and excellent algorithm and its contrast function is optimized by the quadratic convergence of Newton iteration method.In order to improve the convergence speed and the separation precision of the fast ICA,an improved fast ICA algorithm is presented.The algorithm introduces an efficient Newton's iterative method with fifth-order convergence for optimizing the contrast function and gives the detail derivation process and the corresponding condition.The experimental results demonstrate that the convergence speed and the separation precision of the improved algorithm are better than that of the fast ICA. 相似文献
9.
M. Mahdikhani M.H. Kahaei 《AEUE-International Journal of Electronics and Communications》2013,67(1):72-79
We introduce a new algorithm to speed up the ICA algorithm for blind source separation. In this technique, oscillations of the learning curve are first detected and then removed. This leads to making use of a larger step-size parameter and thus a faster ICA. Simulations results show that in average the proposed DR-LCO algorithm is at least three times faster than the ICA, while the quality of separated signals becomes even better. 相似文献
10.
Probabilistic independent component analysis for functional magnetic resonance imaging 总被引:23,自引:0,他引:23
We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses. 相似文献