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Эта публикация цитируется в 1 научной статье (всего в 1 статье)
ОБРАБОТКА ИЗОБРАЖЕНИЙ, РАСПОЗНАВАНИЕ ОБРАЗОВ
Tree-serial parametric dynamic programming with flexible prior model for image denoising
Ph. C. Thangab, A. V. Kopylovc a National Research University Higher School of Economics, 20Myasnitskaya Street, Moscow, Russia
b The University of Da Nang – University of Science and Technology, 54 Nguyen Luong Bang Street, Da Nang, Viet Nam
c Tula State University, pr. Lenina 92, Tula, Russia
Аннотация:
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.
Ключевые слова:
Image denoising, Dynamic programming, Bayesian optimization, Markov random fields (MRFs), Gauss-Seidel iteration method.
Поступила в редакцию: 27.11.2017 Принята в печать: 27.07.2018
Образец цитирования:
Ph. C. Thang, A. V. Kopylov, “Tree-serial parametric dynamic programming with flexible prior model for image denoising”, Компьютерная оптика, 42:5 (2018), 838–845
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/co568 https://www.mathnet.ru/rus/co/v42/i5/p838
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