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231223s2008 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2008.917103
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|a eng
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1 |
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|a Vonesch, C
|e verfasserin
|4 aut
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|a A fast thresholded landweber algorithm for wavelet-regularized multidimensional deconvolution
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|c 2008
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 06.05.2008
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|a Date Revised 08.04.2008
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|a published: Print
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|a Citation Status MEDLINE
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|a We present a fast variational deconvolution algorithm that minimizes a quadratic data term subject to a regularization on the l(1)-norm of the wavelet coefficients of the solution. Previously available methods have essentially consisted in alternating between a Landweber iteration and a wavelet-domain soft-thresholding operation. While having the advantage of simplicity, they are known to converge slowly. By expressing the cost functional in a Shannon wavelet basis, we are able to decompose the problem into a series of subband-dependent minimizations. In particular, this allows for larger (subband-dependent) step sizes and threshold levels than the previous method. This improves the convergence properties of the algorithm significantly. We demonstrate a speed-up of one order of magnitude in practical situations. This makes wavelet-regularized deconvolution more widely accessible, even for applications with a strong limitation on computational complexity. We present promising results in 3-D deconvolution microscopy, where the size of typical data sets does not permit more than a few tens of iterations
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Unser, M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 17(2008), 4 vom: 01. Apr., Seite 539-49
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