&lt;title&gt;Derivation and application of an anisoplanatic optical transfer function for blind deconvolution of laser radar imagery&lt;/title&gt;

MacDonald, Adam; Cain, Stephen

doi:10.1117/12.618109

Cited by 2 publications

(4 citation statements)

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“…Several BSD algorithms have been developed over the last decades, for a review see [37]. BSD shows potentials in the following areas: (i) remote sensing applications; passive radar/sonar processing [38], [39], (ii) image-deblurring, image restoration [40], (iii) speech enhancement using microphone arrays, acoustics [41], [42], [43], [44], (iv) multi-antenna wireless communications, sensor networks [45], [46], (v) biomedical signal-EEG, ECG, MEG (MagnetoEncephaloGraphy), fMRI-analysis [47], [48], [49], (vi) optics [50], and (vii) seismic exploration [51].…”

Section: Introductionmentioning

confidence: 99%

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Controlled Complete ARMA Independent Process Analysis

Szabó

Lörincz

2009

2009 International Joint Conference on Neural Networks

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Abstract-In this paper we address the controlled complete AutoRegressive Moving Average Independent Process Analysis (ARMAX-IPA; X-exogenous input or control) problem, which is a generalization of the Blind SubSpace Deconvolution (BSSD) task. Compared to our previous work that dealt with the undercomplete situation, (i) here we extend the theory to complete systems, (ii) allow an autoregressive part to be present, (iii) and include exogenous control. We investigate the case when the observed signal is a linear mixture of independent multidimensional ARMA processes that can be controlled. Our objective is to estimate the ARMA processes, their driving noises as well as the mixing. We aim efficient estimation by choosing suitable control values. For the optimal choice of the control we adapt the D-optimality principle, also known as the 'InfoMax method'. We solve the problem by reducing it to a fully observable D-optimal ARX task and Independent Subspace Analysis (ISA) that we can solve. Numerical examples illustrate the efficiency of the proposed method. Another extension of the original ICA task is the Blind Source Deconvolution (BSD) problem. Such a problem emerges, for example, at a cocktail-party being held in an echoic room. Several BSD algorithms have been developed over the last decades, for a review see [37]. BSD shows potentials in the following areas: (i) remote sensing applications; passive radar/sonar processing [38], [39], (ii) image- I. INTRODUCTION

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Controlled Complete ARMA Independent Process Analysis

Szabó

Lörincz

2009

2009 International Joint Conference on Neural Networks

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“…See, for example, the review of [19]. Like ICA, BSD has several applications: (i) remote sensing applications; passive radar/sonar processing [20,21], (ii) image-deblurring, image restoration [22], (iii) speech enhancement using microphone arrays, acoustics [23,24,25,26], (iv) multi-antenna wireless communications, sensor networks [27,28], (v) biomedical signal-EEG, ECG, MEG, fMRI-analysis [29,30,31], (vi) optics [32], (vii) seismic exploration [33].…”

Section: Introductionmentioning

confidence: 99%

“…See, for example, the review of Pedersen et al (2007). Like ICA, BSD has several applications: (i) remote sensing applications; passive radar/sonar processing (MacDonald and Cain, 2005;Hedgepeth et al, 1999), (ii) image-deblurring, image restoration (Vural and Sethares, 2006), (iii) speech enhancement using microphone arrays, acoustics (Douglas et al, 2005;Mitianoudis and Davies, 2003;Roan et al, 2003;Araki et al, 2003), (iv) multi-antenna wireless communications, sensor networks (Akyildiz et al, 2002;Deligianni et al, 2006), (v) biomedical signal-EEG, ECG, MEG, fMRI-analysis (Jung et al, 2000;Glover, 1999;Dyrholm et al, 2007), (vi) optics (Kotzer et al, 1998), (vii) seismic exploration (Karslı, 2006).…”

Section: Introductionmentioning

confidence: 99%

Undercomplete Blind Subspace Deconvolution Via Linear Prediction

Szabó

Póczos

Lörincz

Machine Learning: ECML 2007

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We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated 'high dimensional' ISA problem can be handled by a recent technique called joint f-decorrelation (JFD). Similar decorrelation methods have been used previously for kernel independent component analysis (kernel-ICA). More precisely, the kernel canonical correlation (KCCA) technique is a member of this family, and, as is shown in this paper, the kernel generalized variance (KGV) method can also be seen as a decorrelation method in the feature space. These kernel based algorithms will be adapted to the ISA task. In the numerical examples, we (i) examine how efficiently the emerging higher dimensional ISA tasks can be tackled, and (ii) explore the working and advantages of the derived kernel-ISA methods.

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<title>Derivation and application of an anisoplanatic optical transfer function for blind deconvolution of laser radar imagery</title>

Cited by 2 publications

References 7 publications

Controlled Complete ARMA Independent Process Analysis

Controlled Complete ARMA Independent Process Analysis

Undercomplete Blind Subspace Deconvolution Via Linear Prediction

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