Tree-serial parametric dynamic programming with flexible prior model for image denoising
Thang P.C., Kopylov A.V.

National Research University Higher School of Economics, 20Myasnitskaya Street, Moscow, Russia
The University of Da Nang – University of Science and Technology, 54 Nguyen Luong BangStreet, Da Nang, Viet Nam
Tula State University, pr. Lenina 92, Tula, Russia

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Abstract:
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.

Keywords:
Image denoising, Dynamic programming, Bayesian optimization, Markov random fields (MRFs), Gauss-Seidel iteration method.

Citation:
Thang PC, Kopylov AV. Tree-serial parametric dynamic programming with flexible prior model for image denoising. Computer Optics 2018; 42(5): 838-845. DOI: 10.18287/2412-6179-2018-42-5-838-845.

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