A simple overcomplete ICA algorithm by non-orthogonal pair optimizations

Most existing watermark extraction algorithms were dependent on prior knowledge. This paper proposed a blind extraction method without relying on prior knowledge. According to constructing new observation based on nonsubsampled contourlet transform, which utilizes low frequency and directional components of watermarked image, more independent components are generated. We involve these components into watermarked image and resort this solution to multichannel blind source separation. Estimated watermark is recovered by ICA algorithm. Experiment results indicate that the proposed method can achieve better results in contrast with two existing algorithms.

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gpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization

EURASIP Journal on Advances in Signal Processing ◽

10.1155/2007/31951 ◽

2006 ◽

Vol 2007 (1) ◽

Cited By ~ 3

Author(s):

Thang Viet Nguyen ◽

Jagdish Chandra Patra ◽

Sabu Emmanuel

Keyword(s):

Ica Algorithm ◽

Geometric Linearization

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A stable ICA algorithm based on exponent density and Gaussian parametric density mixture models

2011 3rd International Conference on Awareness Science and Technology (iCAST) ◽

10.1109/icawst.2011.6163158 ◽

2011 ◽

Author(s):

Kefeng Wang ◽

Xu Xu ◽

Chonghui Guo

Keyword(s):

Mixture Models ◽

Parametric Density ◽

Ica Algorithm

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Application of Truncated Multipolar Expansion Pair Functions in Atomic Structure Calculations. II. Irreducible Tensor Operator Form of the Strongly Orthogonal Pair Functions. Reduction of Orthogonality Constraints

Canadian Journal of Physics ◽

10.1139/p71-015 ◽

1971 ◽

Vol 49 (1) ◽

pp. 118-132 ◽

Cited By ~ 2

Author(s):

K. Jankowski

Keyword(s):

Closed Shell ◽

Tensor Operator ◽

Irreducible Tensor ◽

Method Of Calculation ◽

Irreducible Tensor Operator ◽

Orthogonality Constraints ◽

Operator Form ◽

Orthogonal Pair ◽

Multipolar Expansion ◽

Pair Function

The truncated multipolar expansion pair functions (TMEPF) defined recently by Jankowski have been used to formulate an approach which takes into account correlation effects within a pair of electrons in the presence of an N electron sea in a closed shell configuration. An irreducible tensor operator form of the strongly orthogonal pair function components has been derived. The analysis of the expressions obtained leads to a systematic reduction of the orthogonality constraints to be imposed on this class of trial functions. The results of this analysis for several types of pair functions have been given. Finally, the method of calculation of matrix elements between orthogonally projected pair function components in terms of radial integrals is presented.

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Regression ICA Algorithm for Image Denoising

Advances in Cognitive Neurodynamics ICCN 2007 ◽

10.1007/978-1-4020-8387-7_171 ◽

2008 ◽

pp. 993-997 ◽

Cited By ~ 1

Author(s):

Shangming Yang ◽

Zhang Yi ◽

Guisong Liu

Keyword(s):

Image Denoising ◽

Ica Algorithm

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Real Time Blind Source Separation in Reverberant Environments

10.26686/wgtn.17008432.v1 ◽

2021 ◽

Author(s):

◽

Timothy Sherry

Keyword(s):

Real Time ◽

Blind Source Separation ◽

Real World ◽

Source Separation ◽

Real World Data ◽

The Core ◽

Reverberant Environments ◽

Ica Algorithm ◽

Stationary Sources ◽

Convolutive Blind Source Separation

<p>An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process.</p>

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