scholarly journals Morozov principle for Kullback-Leibler residual term and Poisson noise

2018 ◽  
Vol 12 (3) ◽  
pp. 607-634
Author(s):  
Bruno Sixou ◽  
◽  
Tom Hohweiller ◽  
Nicolas Ducros
Keyword(s):  
Poisson Noise ◽  
Residual Term

Detection of a Pairwise-Correlated Stream of Signals Against a Poisson Noise Background

1999 ◽  
Vol 53 (7-8) ◽  
pp. 69-78
Author(s):  
A. I. Strelkov ◽  
O. M. Stadnyk ◽  
S. I. Kalmykov ◽  
A. P. Lytyuga
Keyword(s):  
Poisson Noise ◽  
Noise Background

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

Two-Parameter Observation of Correlated Poisson Signals in Poisson Noise

2005 ◽  
Vol 48 (3) ◽  
pp. 215-219 ◽  
Author(s):  
V. M. Kirillov ◽  
L. A. Shcherbakov
Keyword(s):  
Poisson Noise ◽  
Correlated Poisson Signals ◽  
Two Parameter
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