Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm

Hongbin Wang; Paul Miller

doi:10.1109/ISBI.2012.6235917

Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm

Hongbin Wang, Paul Miller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The Richardson-Lucy algorithm is one of the most important algorithms in the image deconvolution area. However, one of its drawbacks is slow convergence. A very significant acceleration is obtained by the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the Image Processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the Heavy-Ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has proof of the convergence rate of O(k-2), where k is the number of iterations. We demonstrate the superior convergence performance of the scaled H-B method on both synthetic and real 3D images.

Original language	English
Title of host publication	Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1743-1746
Number of pages	4
ISBN (Print)	978-1-4577-1857-1
DOIs	https://doi.org/10.1109/ISBI.2012.6235917
Publication status	Published - May 2012
Event	9th IEEE International Symposium on Biomedical Imaging - Barcelona, Spain Duration: 02 May 2012 → 05 May 2012

Conference

Conference	9th IEEE International Symposium on Biomedical Imaging
Country/Territory	Spain
City	Barcelona
Period	02/05/2012 → 05/05/2012

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

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10.1109/ISBI.2012.6235917

Cite this

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title = "Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm",

abstract = "The Richardson-Lucy algorithm is one of the most important algorithms in the image deconvolution area. However, one of its drawbacks is slow convergence. A very significant acceleration is obtained by the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the Image Processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the Heavy-Ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has proof of the convergence rate of O(k-2), where k is the number of iterations. We demonstrate the superior convergence performance of the scaled H-B method on both synthetic and real 3D images.",

author = "Hongbin Wang and Paul Miller",

year = "2012",

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doi = "10.1109/ISBI.2012.6235917",

language = "English",

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booktitle = "Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on",

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address = "United States",

note = "9th IEEE International Symposium on Biomedical Imaging ; Conference date: 02-05-2012 Through 05-05-2012",

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T1 - Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm

AU - Wang, Hongbin

AU - Miller, Paul

PY - 2012/5

Y1 - 2012/5

N2 - The Richardson-Lucy algorithm is one of the most important algorithms in the image deconvolution area. However, one of its drawbacks is slow convergence. A very significant acceleration is obtained by the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the Image Processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the Heavy-Ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has proof of the convergence rate of O(k-2), where k is the number of iterations. We demonstrate the superior convergence performance of the scaled H-B method on both synthetic and real 3D images.

AB - The Richardson-Lucy algorithm is one of the most important algorithms in the image deconvolution area. However, one of its drawbacks is slow convergence. A very significant acceleration is obtained by the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the Image Processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence. In this paper, we introduce the Heavy-Ball (H-B) method for Poisson data optimization and extend it to a scaled H-B method, which includes the BA method as a special case. The method has proof of the convergence rate of O(k-2), where k is the number of iterations. We demonstrate the superior convergence performance of the scaled H-B method on both synthetic and real 3D images.

U2 - 10.1109/ISBI.2012.6235917

DO - 10.1109/ISBI.2012.6235917

M3 - Conference contribution

SN - 978-1-4577-1857-1

SP - 1743

EP - 1746

BT - Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE International Symposium on Biomedical Imaging

Y2 - 2 May 2012 through 5 May 2012

ER -

Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm

Abstract

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ASJC Scopus subject areas

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