DocumentCode :
1341929
Title :
Evaluation and applications of the iterated window maximization method for sparse deconvolution
Author :
Kaaresen, Kjetil F.
Author_Institution :
Dept. of Math., Oslo Univ., Norway
Volume :
46
Issue :
3
fYear :
1998
fDate :
3/1/1998 12:00:00 AM
Firstpage :
609
Lastpage :
624
Abstract :
Estimating a sparse signal from a linearly degraded and noisy data record is often desirable in seismic and ultrasonic applications. Bernoulli-Gaussian modeling and maximum a posteriori estimation has proven successful but entails computationally difficult optimization problems that must be solved by suboptimal methods. The iterated window maximization (IWM) algorithm was proposed for such optimization by Kaaresen (see ibid., vol.45, p.1173-83, 1997). The purpose of this paper is twofold. First, the IWM is evaluated against several established alternatives for Bernoulli-Gaussian deconvolution. The restoration quality is quantified by various loss functions, and the average performance is studied through simulation. In all cases examined, the IWM combined better restoration and significantly faster execution than the other algorithms. This motivates extension of IWM to other models, which is the second objective of this paper. Promising real data results are obtained from such diverse applications as robust modeling of ultrasonic nondestructive evaluation data, deblurring of two-dimensional (2-D) astronomical star fields, and segmentation of seismic well logs. It is also argued that IWM can be used for other deconvolution problems as long as the function to be reconstructed is, in some sense, sparse
Keywords :
Gaussian processes; astronomy; deconvolution; geophysical signal processing; image segmentation; iterative methods; maximum likelihood estimation; noise; optimisation; seismology; signal restoration; ultrasonic materials testing; 2D astronomical star fields deblurring; Bernoulli-Gaussian deconvolution; Bernoulli-Gaussian modeling; average performance; iterated window maximization method; linearly degraded record; loss functions; maximum a posteriori estimation; noisy data record; optimization; optimization problems; real data results; restoration quality; robust modeling; seismic applications; seismic well logs segmentation; simulation; sparse deconvolution; sparse signal estimation; ultrasonic applications; ultrasonic nondestructive evaluation data; Acoustic applications; Amplitude estimation; Deconvolution; Degradation; Gaussian processes; Maximum a posteriori estimation; Optimization methods; Performance loss; Robustness; Signal processing algorithms;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.661329
Filename :
661329
Link To Document :
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