Abstract
This paper addresses the topic of image colorization that consists in converting a gray-scale image into a color one. In the literature, there exist two main types of approaches to tackle this problem. The first one is the manual methods where the color information is given by some scribbles drawn by the user on the image. The interest of these approaches comes from the interactions with the user that can put any color he wants. Nevertheless, when the scene is complex many scribbles must be drawn and the interactive process becomes tedious and time-consuming. The second category of approaches is the exemplar-based methods that require a color image as input. Once the example image is given, the colorization is generally fully automatic. A limitation of these methods is that the example image needs to contain all the desired colors in the final result. In this paper, we propose a new framework that unifies these two categories of approaches into a joint variational model. Our approach is able to take into account information coming from any colorization method among these two categories. Experiments and comparisons demonstrate that the proposed approach provides competitive colorization results compared to state-of-the-art methods.
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Gonzales, R.C., Wintz, P.: Digital Image Processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (1987)
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. on Graphics 23(3), 689–694 (2004)
Yatziv, L., Sapiro, G.: Fast image and video colorization using chrominance blending. IEEE Trans. on Image Processing 15(5), 1120–1129 (2006)
Heu, J., Hyun, D.Y., Kim, C.S., Lee, S.U.: Image and video colorization based on prioritized source propagation. In: Proc. of ICIP (2009)
Lagodzinski, P., Smolka, B.: Digital image colorization based on probabilistic distance transformation. Proc. of ELMAR. 2, 495–498 (2008)
Kim, T.H., Lee, K.M., Lee, S.U.: Edge-preserving colorization using data-driven random walks with restart. In: Proc. of ICIP, pp. 1661–1664 (2010)
Kawulok, M., Kawulok, J., Smolka, B.: Discriminative textural features for image and video colorization. IEICE Trans. on Information and Systems 95(7), 1722–1730 (2012)
Drew, M.S., Finlayson, G.D.: Improvement of colorization realism via the structure tensor. Int. Jour. on Image Graphics 11(4), 589–609 (2011)
Lezoray, O., Ta, V.T., Elmoataz, A.: Nonlocal graph regularization for image colorization. In: Proc. of ICPR (2008)
Ding, X., Xu, Y., Deng, L., Yang, X.: Colorization using quaternion algebra with automatic scribble generation. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 103–114. Springer, Heidelberg (2012)
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. on Graphics 21(3), 277–280 (2002)
Wei, L.Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: ACM Comp. Graphics and Interactive Techniques, pp. 479–488 (2000)
Irony, R., Cohen-Or, D., Lischinski, D.: Colorization by example. In: Eurographics Conference on Rendering Techniques, Eurographics Association, pp. 201–210 (2005)
Gupta, R.K., Chia, A.Y.S., Rajan, D., Ng, E.S., Zhiyong, H.: Image colorization using similar images. In: ACM Int. Conf. on Multimedia, pp. 369–378 (2012)
Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proc. of ICCV, pp. 10–17 (2003)
Charpiat, G., Hofmann, M., Schölkopf, B.: Automatic image colorization via multimodal predictions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 126–139. Springer, Heidelberg (2008)
Chen, T., Wang, Y., Schillings, V., Meinel, C.: Grayscale image matting and colorization. In: Proc. of ACCV, pp. 1164–1169 (2004)
Bugeau, A., Ta, V.T., Papadakis, N.: Variational exemplar-based image colorization. IEEE Trans. on Image Processing 23(1), 298–307 (2014)
Pierre, F., Aujol, J.F., Bugeau, A., Ta, V.T.: Hue constrained image colorization in the RGB space. Preprint (2014)
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. Jour. of Math. Imag. and Vis. 40(1), 120–145 (2011)
Bresson, X., Chan, T.F.: Fast dual minimization of the vectorial total variation norm and applications to color image processing. Inverse Problems and Imaging 2(4), 455–484 (2008)
Sethian, J.A.: Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science 3. Cambridge University Press (1999)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)
Peyré, G.: Toolbox fast marching - a toolbox for fast marching and level sets computations (2008)
Chen, Y., Ye, X.: Projection onto a simplex. arXiv preprint (2011). arXiv:1101.6081
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Pierre, F., Aujol, JF., Bugeau, A., Ta, VT. (2015). A Unified Model for Image Colorization. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8927. Springer, Cham. https://doi.org/10.1007/978-3-319-16199-0_21
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