Scaled Heavy-Ball Acceleration of the Richardson-Lucy Algorithm for 3D Microscopy Image Restoration

Author

Hongbin Wang ; Miller, Paul C.

Author_Institution

Dept. of Commun. & Inf. Technol., Queen´s Univ. Belfast, Belfast, UK

Volume

23

Issue

2

fYear

2014

fDate

Feb. 2014

Firstpage

848

Lastpage

854

Abstract

The Richardson-Lucy algorithm is one of the most important in image deconvolution. However, a drawback is its slow convergence. A significant acceleration was obtained using 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 a proof of the convergence rate of O(K^-2), where k is the number of iterations. We demonstrate the superior convergence performance, by a speedup factor of five, of the scaled H-B method on both synthetic and real 3D images.

Keywords

deconvolution; image restoration; iterative methods; optimisation; stochastic processes; 3D microscopy image restoration; BA method; Poisson data optimization; Richardson-Lucy algorithm; convergence rate; image deconvolution; image processing MATLAB toolbox; iterative method; real 3D images; scaled H-B method; scaled heavy-ball acceleration; superior convergence performance; synthetic images; Acceleration; Barium; Convergence; Image restoration; Noise; Three-dimensional displays; Vectors; Deconvolution; Poisson noise; Richardson-Lucy algorithm; heavy-ball acceleration;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/TIP.2013.2291324

Filename

6665057