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RESEARCH PRODUCT

Deconvolution by Regularized Matching Pursuit

Amir AverbuchPekka NeittaanmäkiValery A. Zheludev

subject

Blind deconvolutionSpline (mathematics)WaveletComputer scienceSpline waveletOblique projectionDeconvolutionGreedy algorithmMatching pursuitAlgorithm

description

In this chapter, an efficient method that restores signals from strongly noised blurred discrete data is presented. The method can be characterized as a Regularized Matching Pursuit (RMP), where dictionaries consist of spline wavelet packets. It combines ideas from spline theory, wavelet analysis and greedy algorithms. The main distinction from the conventional matching pursuit is that different dictionaries are used to test the data and to approximate the solution. In addition, oblique projections of data onto dictionary elements are used instead of orthogonal projections, which are used in the conventional Matching Pursuit (MP). The slopes of the projections and the stopping rule for the algorithm are determined automatically.

yearjournalcountryeditionlanguage
2014-01-01
https://doi.org/10.1007/978-94-017-8926-4_10