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

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

Автор: Thang Pham Cong, Kopylov Andrei Valerievich

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 5 т.42, 2018 года.

Бесплатный доступ

We consider here image denoising procedures, based on computationally effective tree-serial pa-rametric 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 pref-erences. 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.

Image denoising, dynamic programming, bayesian optimization, markov random fields (mrfs), gauss-seidel iteration method

Короткий адрес: https://sciup.org/140238485

IDR: 140238485   |   DOI: 10.18287/2412-6179-2018-42-5-838-845

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