Abstract
Colorization is a technique to automatically produce color components for monochrome images and videos based on a few input colors. Generally, image colorization is initialized from a number of seed pixels whose colors are specified by users, and then the colors are gradually prorogating to the monochrome surroundings under a given optimization constraint. So, the performance of colorization is highly dependent on the selection of seed pixels. However, little attention has been paid to the selection of seed pixels, and how to improve the effectiveness of manual input remains a challenging task. To address this, an improved colorization method using seed pixel selection is proposed to assist the users in determining which pixels are highly required to be colorized for a high-quality colorized image. Specifically, the gray-scale image is first divided into non-overlapped blocks, and then, for each block, two pixels that approximate the average luminance of block are selected as the seeds. After the seed pixels are colored by users, an optimization that minimizes the difference between the seeds and their adjacent pixels is employed to propagate the colors to the other pixels. The experimental results demonstrate that, for a given amount of inputs, the proposed method can achieve a higher PSNR than the conventional colorization methods.
Similar content being viewed by others
References
An X, Pellacini F (2008) Appprop: all-pairs appearance-space edit propagation. ACM Trans Graph 27(3):15–19
Anagnostopoulos N, Iakovidou C, Amanatiadis A, Boutalis Y, Chatzichristofis S (2014) Two-staged image colorization based on salient contours. In: International Conference on Imaging Systems and Techniques, IEEE, pp 381–385
Balinsky A, Mohammad N Sparse natural image statistics and their applications to colorization and compression. In: International Conference on Image Processing
Bezerra H, Feijó B, Velho L (2006) A computer-assisted colorization algorithm based on topological difference. In: Brazil Ian symposium on computer graphics and image processing, IEEE
Bugeau A, Ta V-T (2012) Patch-based image colorization. In: International Conference on Pattern Recognition, IEEE, pp 3058–3061
Bugeau A, Ta V-T, Papadakis N (2014) Variational exemplar-based image colorization. Trans Image Process 23(1):298–307
Chaumont M, Puech W (2008) Attack by colorization of a grey-level image hiding its color palette. In: International Conference on Multimedia & Expo, IEEE, pp 1537–1540
Chen X, Zou D, Zhao Q, Tan P (2012) Manifold preserving edit propagation. ACM Trans Graph 31(6):439–445
Chen X, Li J, Zou D, Zhao Q (2016) Learn sparse dictionaries for edit propagation. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 25(4):1688–1698
Devi MS, Mandowara A (2012) Extended performance comparison of pixel window size for colorization of grayscale images using yuv color space. In: Nirma University International Conference on Engineering, IEEE, pp 1–5
Drew MS, Finlayson GD (2008) Realistic colorization via the structure tensor. In: International Conference on Image Processing, IEEE, pp 457–460
Du W (2012) Colorization using the information of prototypes and edges. In: International Conference on Intelligent Control and Information Processing, IEEE, pp 644–647
Gondal I, Murshed M et al (2010) Scarf: Semi-automatic colorization and reliable image fusion
Gunel M, Karacan L, Erdem AT, Erdem E (2014) Image colorization via dense correspondences. In: Signal Processing and Communications Applications Conference, IEEE, pp 285–288
Heu J-H, Hyun D-Y, Kim C-S, Lee S-U (2009) Image and video colorization based on prioritized source propagation. In: International Conference on Image Processing, IEEE, pp 465–468
Irony R, Cohen-Or D, Lischinski D (2005) Colorization by example Eurographics symp on rendering, vol. 2, citeseer
Jacob VG, Gupta S (2009) Colorization of grayscale images and videos using a semiautomatic approach. In: International Conference on Image Processing, IEEE, pp 1653–1656
Kawulok M, Smolka B (2010) Competitive image colorization. In: International Conference on Image Processing, IEEE, pp 405–408
Kim TH, Lee KM, Lee SU (2009) Edge-preserving colorization using data-driven random walks with restart. In: International Conference on Image Processing, IEEE, pp 1661–1664
Kumar S, Swarnkar A (2012) Gray image colorization in ycbcr color space. In: International conference on emerging technology trends in electronics, Communication and Networking, IEEE, pp 1–6
Kumar S, Swarnkar A (2012) Colorization of gray scale images in l α β color space using mean and standard deviation. In: Students’ conference on electrical, Electronics and Computer Science, IEEE, pp 1–4
Lee S, Park S-W, Oh P, Kang MG (2013) Colorization-based compression using optimization. Trans Image Process 22(7):2627–2636
Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. In: Transactions on Graphics, Vol. 23, ACM, pp 689–694
Lezoray O, Ta VT, Elmoataz A (2008) Nonlocal graph regularization for image colorization. In: International Conference on Pattern Recognition, IEEE, pp 1–4
Liu X, Wan L, Qu Y, Wong T-T, Lin S, Leung C-S, Heng P-A (2008) Intrinsic colorization. In: Transactions on Graphics, Vol. 27, ACM, p 152
Luan Q, Wen F, Cohen-Or D, Liang L, Xu Y-Q, Shum H-Y (2007) Natural image colorization. In: Eurographics conference on Rendering Techniques, Eurographics Association, pp 309–320
Nakajima Y, Ueno T, Yoshida T, Ikehara M (2013) Colorization based on piecewise autoregressive model. In: Asilomar conference on signals, Systems and Computers, IEEE, pp 1990–1994
Nie D, Ma Q, Ma L, Xiao S (2007) Optimization based grayscale image colorization. Pattern recognition letters 28(12):1445–1451
Pang J, Au OC, Tang K, Guo Y (2013) Image colorization using sparse representation. In: International conference on acoustics, Speech and Signal Processing, IEEE, pp 1578–1582
Pang J, Au OC, Yamashita Y, Ling Y, Guo Y, Zeng J (2014) Self-similarity-based image colorization. In: International Conference on Image Processing, IEEE, pp 4687–4691
Pellacini F, Lawrence J (2007) Appwand: editing measured materials using appearance-driven optimization. ACM Trans Graph 26(3):54
Pierre F, Aujol J-F, Bugeau A, Papadakis N, Ta V-T (2015) Luminance-chrominance model for image colorization. Journal on Imaging Sciences 8 (1):536–563
Rusu C, Tsaftaris S et al (2013) Estimation of scribble placement for painting colorization. In: International Symposium on Image and Signal Processing and Analysis, IEEE, pp 564–569
Ryu T, Wang P, Lee S-H (2013) Image compression with meanshift based inverse colorization. In: International conference on consumer electronics
Sheng B, Sun H, Chen S, Liu X, Wu E (2011) Colorization using the rotation-invariant feature space. Computer Graphics and Applications (2):24–35
Sỳkora D, Buriánek J, žára J (2004) Unsupervised colorization of black-and-white cartoons. In: Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering, ACM, pp 121–127
Thepade SD, Garg RH, Ghewade SA, Jagdale PA, Mahajan NM (2015) Performance assessment of assorted similarity measures in gray image colorization using lbg vector quantization algorithm. In: International Conference on Industrial Instrumentation and Control, IEEE, pp 332–337
Uruma K, Konishi K, Takahashi T, Furukawa T (2014) Image colorization algorithm using series approximated sparse function. In: International conference on acoustics, Speech and Signal Processing, IEEE, pp 1215–1219
Wang H, Gan Z, Zhang Y, Zhu X (2012) Novel colorization method based on correlation neighborhood similarity pixels priori. In: International Conference on Signal Processing, Vol. 2, IEEE, pp 885–888
Wang S, Zhang Z (2012) Colorization by matrix completion. In: AAAI, Citeseer
Welsh T, Ashikhmin M, Mueller K (2002) Transferring color to grayscale images. Transaction on Graphic 21(3):277–280
Xie D-e, Xuan Y, Zhang Z (2010) A colored pencil-drawing generating method based on interactive colorization. In: International Conference on Computing, Control and Industrial Engineering, Vol. 2, IEEE, pp 166–169
Yatziv L, Sapiro G (2006) Fast image and video colorization using chrominance blending. Trans Image Process 15(5):1120–1129
Acknowledgments
This work is supported by the National Science Foundation of China (Nos. 61502160, 61502158, 61472131, 61572182), the Science and Technology Key Projects of Hunan Province (2015 TP1004), and the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hu, M., Ou, B. & Xiao, Y. Efficient image colorization based on seed pixel selection. Multimed Tools Appl 76, 23567–23588 (2017). https://doi.org/10.1007/s11042-016-4112-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-4112-9