Scheduled System Maintenance on December 17th, 2014:
IEEE Xplore will be upgraded between 2:00 and 5:00 PM EST (18:00 - 21:00) UTC. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Evaluation and applications of the iterated window maximization method for sparse deconvolution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Kaaresen, K.F. ; Dept. of Math., Oslo Univ., Norway

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

Published in:

Signal Processing, IEEE Transactions on  (Volume:46 ,  Issue: 3 )