The aim of this paper is to consider a modification of a block coordinate gradient projection method with Armijo linesearch along the descent direction in which the projection on the feasible set is performed according to a variable non Euclidean metric. The stationarity of the limit points of the resulting scheme has recently been proved under some general assumptions on the generalized gradient projections employed. Here we tested some examples of methods belonging to this class on a blind deconvolution problem from data affected by Poisson noise, and we illustrate the impact of the projection operator choice on the practical performances of the corresponding algorithm.

Application of cyclic block generalized gradient projection methods to poisson blind deconvolution / Rebegoldi S.; Bonettini S.; Prato M.. - ELETTRONICO. - (2015), pp. 225-229. (Intervento presentato al convegno 23rd European Signal Processing Conference, EUSIPCO 2015 tenutosi a Nice Congress Center, fra nel 2015) [10.1109/EUSIPCO.2015.7362378].

Application of cyclic block generalized gradient projection methods to poisson blind deconvolution

Rebegoldi S.
Membro del Collaboration Group
;
2015

Abstract

The aim of this paper is to consider a modification of a block coordinate gradient projection method with Armijo linesearch along the descent direction in which the projection on the feasible set is performed according to a variable non Euclidean metric. The stationarity of the limit points of the resulting scheme has recently been proved under some general assumptions on the generalized gradient projections employed. Here we tested some examples of methods belonging to this class on a blind deconvolution problem from data affected by Poisson noise, and we illustrate the impact of the projection operator choice on the practical performances of the corresponding algorithm.
2015
2015 23rd European Signal Processing Conference, EUSIPCO 2015
23rd European Signal Processing Conference, EUSIPCO 2015
Nice Congress Center, fra
2015
Goal 9: Industry, Innovation, and Infrastructure
Rebegoldi S.; Bonettini S.; Prato M.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1188720
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