A discrepancy principle for Poisson data

Inverse Problems

Volumn 26, Issue 10, 2010, Pages

  Bertero, M ^a   Boccacci, P ^a   Talenti, G ^b   Zanella, R ^c   Zanni, L ^c  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF FLORENCE   (Italy)

^c UNIVERSITY OF MODENA AND REGGIO EMILIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN PARADIGM; COUNTING PROCESS; DE-NOISING; DEBLURRING PROBLEMS; DISCREPANCY PRINCIPLE; EMISSION TOMOGRAPHY; GENERALIZED KULLBACK-LEIBLER DIVERGENCE; ILL-CONDITIONED; IMAGE INTENSITIES; IMAGING MATRICES; INCIDENT PARTICLES; MAXIMIZATION PROBLEM; MAXIMUM-LIKELIHOOD APPROACH; NUMERICAL EXPERIMENTS; POISSON DATA; POISSON RANDOM VARIABLES; REGULARIZATION FUNCTION; REGULARIZATION PARAMETERS; STATISTICAL PROPERTIES; TIKHONOV REGULARIZATION;

FLUORESCENCE MICROSCOPY; IMAGE RECONSTRUCTION; MAXIMUM LIKELIHOOD; MULTIPHOTON PROCESSES; NUMERICAL ANALYSIS; RANDOM VARIABLES; TOMOGRAPHY;

PARAMETERIZATION;

EID: 78049442605     PISSN: 02665611     EISSN: 13616420     Source Type: Journal    
DOI: 10.1088/0266-5611/26/10/105004     Document Type: Article

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