Abstract
Given a grayscale photograph, this paper tackles the issue of fantasizing about a conceivable shading rendition of the photograph. This issue is underconstrained, so past methodologies have either depended on significant human cooperation or came about in desaturated colorizations. We propose a wholly automated approach that produces lively furthermore, sensible colorizations. This approach utilizes the combination of CNN-based colorization and parameter-free k-means clustering to identify the color spills, so that the image can be recolored to produce color images that are aesthetically more pleasing and plausible than the images produced by the state-of-the-art methods. The performance of the proposed model is evaluated in terms of mean square error and structural similarity and found to be superior to the related works.
Similar content being viewed by others
References
Vieira, L.F.M., Vilela, R.D., Nascimento, E.R.D., Fernandes, F.A., Carceroni, R.L. and Araújo, A.D.A.: Automatically choosing source color images for coloring grayscale images. In: 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003), pp. 151–158. IEEE (2003)
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision, Amsterdam, Netherlands, pp. 649–666 (2016)
Zhang, R., Zhu, Y., Isola, P., Geng, X., Lin, A. S., Yu, T, Efros, A.A.: Real-Time User-Guided Image Colorization with Learned Deep Priors (2017). https://arxiv.org/abs/1705.02999v1
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM SIGGRAPH 2004 Papers. Los Angeles, USA, vol. 14(1), pp. 689–694 (2004)
Huang, Y.C., Tung, Y.S., Chen, J.C. et al.: An adaptive edge detection based colorization algorithm and its applications. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, Singapore, vol. 14(1), pp. 351–354 (2005)
Yatziv, L., Sapiro, G.: Fast image and video colorization using chrominance blending. IEEE Trans. Img. Proc. 15(5), 1120–1129 (2006)
Qu, Y., Wong, T.-T., Heng, P.-A.: Natural image colorization. ACM SIGGRAPH 2006 Papers, SIGGRAPH 06, Boston, USA, vol. 3, pp. 1214–1220 (2006)
Luan, Q., Wen, F., Cohen-Or, D., et al.: Natural image colorization. In: Proceedings of the 18th Eurographics Conf. on Rendering Techniques, EGSR 07, Grenoble, France, vol. 42(3), pp. 309–320 (2007)
Charpiat, G., Hofmann, M., Scholkopf, B.: Automatic image colorization via multimodal predictions. In: European Conference on Computer Vision ECCV, pp. 126–139 (2008)
Gupta, R.K., Chia, A.Y.-S., Rajan, D., et al.: Image colorization using similar images. ACM Int. Conf. Multimedia 1, 369–378 (2012)
Liu, X., Wan, L., Qu, Y., et al.: Intrinsic colorization. Trans. Graph. 27, 152 (2008)
Chia, A.Y.-S., Zhuo, S., Gupta, R.K., et al.: Semantic colorization with internet images. ACM Trans. Graph. 30(156), 1–8 (2011)
Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: International Conference on Computer Vision (ICCV), Las Condes, Chile, vol. 1 (2015)
Deshpande, A., Rock, J., Forsyth, D.: Learning large-scale automatic image colorization. In: International Conference on Computer Vision (ICCV), Las Condes, Chile, vol. 1 (2015)
Larsson, G., Gustav, M.M., Shakhnarovich, G.: Learning representations for automatic colorization. In: European Conference on Computer Vision, Amsterdam, Netherlands, pp. 577–593 (2016)
Iizuka, S., Edgar, S.S., Ishikawa, H.: Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graph. (TOG) 35(4), 110 (2016)
Liu, H., Fu, Z., Han, J., Shao, L., Liu, H.: Single satellite imagery simultaneous super-resolution and colorization using multi-task deep neural networks. J. Vis. Commun. Image Represent. 53, 20–30 (2018)
Hussein, A.A., Yang, X.: Colorization using edge-preserving smoothing filter. SIViP 8(8), 1681–1689 (2014)
Ju, H.J., Lee, D.K., Park, R.H.: Color fringe removal in narrow color regions of digital images. SIViP 8(8), 1651–1662 (2014)
Dong, X., Liu, C., Li, W., Hu, X., Wang, X., Wanga, Y.: Self-supervised colorization towards monochrome-color camera systems using cycle CNN. IEEE Trans. Image Process. 30, 6609–6622 (2021)
Kong, G., Tian, H., Duan, X., Long, H.: Adversarial edge-aware image colorization with semantic segmentation. IEEE Access 9, 28194–28203 (2021)
Nguyen-Quynh, T.T., Kim, S.H., Do, N.T.: Image colorization using the global scene-context style and pixel-wise semantic segmentation. IEEE Access 8, 214098–214114 (2020)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics (2007).
Davies, D. L., Bouldin, D. W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2): 224–227 (1979)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint https://arxiv.org/abs/1409.1556
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Singh, N., Gupta, G., Singh, A. et al. Removal of spatial inconsistencies in automated image colorization using parameter-free clustering and convolutional neural networks. SIViP 16, 1011–1018 (2022). https://doi.org/10.1007/s11760-021-02047-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-021-02047-5