Variational Exemplar-Based Image Colorization - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Image Processing Year : 2014

Variational Exemplar-Based Image Colorization

Abstract

In this paper, we address the problem of recovering a color image from a grayscale one. The input color data comes from a source image considered as a reference image. Reconstructing the missing color of a grayscale pixel is here viewed as the problem of automatically selecting the best color among a set of colors candidates while simultaneously ensuring the local spatial coherency of the reconstructed color information. To solve this problem, we propose a variational approach where a specific energy is designed to model the color selection and the spatial constraint problems simultaneously. The contributions of this paper are twofold: first, we introduce a variational formulation modeling the color selection problem under spatial constraints and propose a minimization scheme which allows computing a local minima of the defined non-convex energy. Second, we combine different patch-based features and distances in order to construct a consistent set of possible color candidates. This set is used as input data and our energy minimization allows to automatically select the best color to transfer for each pixel of the grayscale image. Finally, experiments illustrate the potentiality of our simple methodology and show that our results are very competitive with respect to the state-of-the-art methods.
Fichier principal
Vignette du fichier
ip2013_preprint.pdf (1.65 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00803219 , version 1 (21-03-2013)

Identifiers

Cite

Aurélie Bugeau, Vinh-Thong Ta, Nicolas Papadakis. Variational Exemplar-Based Image Colorization. IEEE Transactions on Image Processing, 2014, 23 (1), ⟨10.1109/TIP.2013.2288929⟩. ⟨hal-00803219⟩
273 View
1091 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More