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Journal of Scientific Computing

Erschienen in:

15.05.2018

Image Colorization Based on a Generalization of the Low Dimensional Manifold Model

verfasst von: Myeongmin Kang, Myungjoo Kang, Miyoun Jung

Erschienen in: Journal of Scientific Computing | Ausgabe 2/2018

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Abstract

In this paper, we introduce a novel model that restores a color image from a grayscale image with color values given in small regions. The model is based on the idea of the generalization of the low dimensional manifold model (Shi et al. in J Sci Comput, 2017. https://doi.org/10.1007/s10915-017-0549-x) and the YCbCr color space. It involves two prior terms, a weighted nonlocal Laplacian (WNLL) and a weighted total variation (WTV). The WNLL allows regions without color information to be interpolated smoothly from given sparse color data, while the WTV assists to inhibit the diffusion of color values across edges. To cope with various types of sampled data, we introduce an updating rule for the weight function in the WNLL. Furthermore, we present an efficient iterative algorithm for solving the proposed model. Lastly, numerical experiments validate the superior performance of the proposed model over that of the other state-of-the-art models.

Vorheriger Artikel A Double-Layer Reduced Model for Fault Flow on Slipping Domains with an Hybrid Finite Volume Scheme

Nächster Artikel Approximate Homogenization of Fully Nonlinear Elliptic PDEs: Estimates and Numerical Results for Pucci Type Equations

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Titel: Image Colorization Based on a Generalization of the Low Dimensional Manifold Model
verfasst von: Myeongmin Kang
Myungjoo Kang
Miyoun Jung
Publikationsdatum: 15.05.2018
Verlag: Springer US
Erschienen in: Journal of Scientific Computing / Ausgabe 2/2018
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI: https://doi.org/10.1007/s10915-018-0732-8

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