Tree-serial parametric dynamic programming with flexible prior model for image denoising

Tree-serial parametric dynamic programming with flexible prior model for image denoising

We consider here image denoising procedures, based on computationally effective tree-serial parametric dynamic programming procedures, different representations of an image lattice by the set of acyclic graphs and non-convex regularization of a new type which allows to flexibly set a priori preferen...

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Bibliographic Details
Main Authors: Pham Cong Thang, Kopylov Andrei Valerievich
Format: Article
Language:English
Published: Samara National Research University 2018-10-01
Series:Компьютерная оптика
Subjects:
Image denoising
Dynamic programming
Bayesian optimization
Markov random fields (MRFs)
Gauss-Seidel iteration method
Online Access:http://computeroptics.smr.ru/KO/PDF/KO42-5/420515.pdf
Description
Summary:We consider here image denoising procedures, based on computationally effective tree-serial parametric dynamic programming procedures, different representations of an image lattice by the set of acyclic graphs and non-convex regularization of a new type which allows to flexibly set a priori preferences. Experimental results in image denoising, as well as comparison with related methods, are provided. A new extended version of multi quadratic dynamic programming procedures for image denoising, proposed here, shows an improved accuracy for images of a different type.
ISSN:0134-2452
2412-6179

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