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Image Colorization: A Survey of Methodolgies and Techniques

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021 (AISI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 100))

Abstract

Image Colorization is the problem of defining colors for grayscale images. Deep neural networks proved a great success in different fields recently. Therefore, it is used to solve the image colorization problem; moreover, it proved to be a very good choice. In literature, few review papers addressed the colorization problem. Most of them classified the research papers according to one or two criteria as input image type, a number of colored output images, colorization methodology, techniques or networks used in colorization, and network paths. This review classifies the papers according to these criteria intagrally and with a relatively large number of papers. Besides, the review displays the commonly used datasets and measures of comparison. It is found that deep learning is a widely used solution methodology to the problem. Unifying the comparison measures and data sets might help show the advances of the new models.

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References

  1. Cao, L., Shang, Y., Zhao, J., Li, Z.: Comparison of grayscale image colorization methods in different color spaces. In: Zhao, P., Ouyang, Y., Xu, M., Yang, Li., Ren, Y. (eds.) Advances in Graphic Communication, Printing and Packaging. LNEE, vol. 543, pp. 290–300. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3663-8_40

    Chapter  Google Scholar 

  2. Nah, S., et al.: NTIRE 2019 challenge on image colorization: report. In: IEEE Computing Socitey Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, pp. 2233–2240 (2019). https://doi.org/10.1109/CVPRW.2019.00276

  3. Anwar, S., Tahir, M., Li, C., Mian, A., Shahbaz Khan, F., Wahab Muzaffar, A.: Image colorization: a survey and dataset. ar**v, pp. 1–19 (2020)

    Google Scholar 

  4. Pierre, F., et al.: Recent approaches for image colorization (2020)

    Google Scholar 

  5. Chakraborty, S.: Image colourisation using deep feature-guided image retrieval. IET Image Process. 13(7), 1130–1137 (2019). https://doi.org/10.1049/iet-ipr.2018.6169

    Article  Google Scholar 

  6. Li, F., Ng, M.K.: Image colorization by using graph bi-Laplacian. Adv. Comput. Math. 45(3), 1521–1549 (2019). https://doi.org/10.1007/s10444-019-09677-x

    Article  MathSciNet  MATH  Google Scholar 

  7. Fang, L., Wang, J., Lu, G., Zhang, D., Fu, J.: Hand-drawn grayscale image colorful colorization based on natural image. Vis. Comput. 35(11), 1667–1681 (2018). https://doi.org/10.1007/s00371-018-1613-8

    Article  Google Scholar 

  8. Tan, P., Pierre, F., Nikolova, M.: Inertial alternating generalized forward–backward splitting for image colorization. J. Math. Imaging Vis. 61(5), 672–690 (2019). https://doi.org/10.1007/s10851-019-00877-0

    Article  MathSciNet  MATH  Google Scholar 

  9. **, Z., Min, L., Ng, M.K., Zheng, M.: Image colorization by fusion of color transfers based on DFT and variance features. Comput. Math. with Appl. 77(9), 2553–2567 (2019). https://doi.org/10.1016/j.camwa.2018.12.033

    Article  MathSciNet  MATH  Google Scholar 

  10. Bao, B., Fu, H.: Scribble-based colorization for creating smooth-shaded vector graphics. Comput. Graph. 81, 73–81 (2019). https://doi.org/10.1016/j.cag.2019.04.003

    Article  Google Scholar 

  11. Sugawara, M., Uruma, K., Hangai, S., Hamamoto, T.: Local and global graph approaches to image colorization. IEEE Signal Process. Lett. 27, 765–769 (2020). https://doi.org/10.1109/LSP.2020.2994817

    Article  Google Scholar 

  12. Min, L., Li, Z., **, Z., Cui, Q.: Color edge preserving image colorization with a coupled natural vectorial total variation. Comput. Vis. Image Underst. 196(April), 102981 (2020). https://doi.org/10.1016/j.cviu.2020.102981

    Article  Google Scholar 

  13. Fang, F., Wang, T., Zeng, T., Zhang, G.: A superpixel-based variational model for image colorization. IEEE Trans. Vis. Comput. Graph. 26(10), 2931–2943 (2020). https://doi.org/10.1109/TVCG.2019.2908363

    Article  Google Scholar 

  14. Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. In: Proceedings of 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2002, pp. 277–280 (2002). https://doi.org/10.1145/566570.566576

  15. Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph. 23(3), 689–694 (2004). https://doi.org/10.1145/1015706.1015780

    Article  Google Scholar 

  16. Morimoto, Y., Taguchi, Y., Naemura, T.: Automatic colorization of grayscale images using multiple images on the web. In: SIGGRAPH 2009 Posters, SIGGRAPH 2009, p. 60558 (2009). https://doi.org/10.1145/1599301.1599333

  17. Charpiat, G., et al.: Automatic image colorization via multimodal predictions. To Cite This Version: Automatic Image Colorization via Multimodal Predictions (2010)

    Google Scholar 

  18. Chia, A.Y.S., et al.: Semantic colorization with internet images. In: Proceedings of 2011 SIGGRAPH Asia Conference, SA 2011, no. May 2017, pp. 1–8 (2011). https://doi.org/10.1145/2070752.2024190

  19. Liu, S., Zhang, X.: Automatic grayscale image colorization using histogram regression. Pattern Recognit. Lett. 33(13), 1673–1681 (2012). https://doi.org/10.1016/j.patrec.2012.06.001

    Article  Google Scholar 

  20. Sousa, A., Kabirzadeh, R., Blaes, P.: Automatic colorization of grayscale images. In: 3rd International Conference on Recent Trends Engineering Technology (ICRTET 2014), vol. 1, no. ELSEVIER 2014 (2014). http://cs229.stanford.edu/proj2013/KabirzadehSousaBlaes-AutomaticColorizationOfGrayscaleImages.pdf

  21. Trémea, A., Schettini, R., Tominaga, S.: Descriptor-based image colorization and regularization. In: Trémeau, A., Schettini, R., Tominaga, S. (eds.) Computational Color Imaging. CCIW 2015. Lecture Notes in Computer Science, vol 9016, pp. 59–68. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15979-9_6

    Chapter  Google Scholar 

  22. Zanoci, C., Andress, J.: From grayscale to color : digital image colorization using machine learning, pp. 1–6 (2015)

    Google Scholar 

  23. Deshpande, A., Rock, J., Forsyth, D.: Learning large-scale automatic image colorization. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2015 Inter, pp. 567–575 (2015). https://doi.org/10.1109/ICCV.2015.72

  24. Pierre, F., Aujol, J.F., Bugeau, A., Papadakis, N., Ta, V.T.: Luminance-chrominance model for image colorization∗. SIAM J. Imaging Sci. 8(1), 536–563 (2015). https://doi.org/10.1137/140979368

    Article  MathSciNet  MATH  Google Scholar 

  25. Hasnat, A., Halder, S., Bhattacharjee, D., Nasipuri, M.: A proposed grayscale face image colorization system using particle swarm optimization. Int. J. Virtual Augment. Real. 1(1), 72–89 (2017). https://doi.org/10.4018/ijvar.2017010106

    Article  Google Scholar 

  26. Li, B., Zhao, F., Su, Z., Liang, X., Lai, Y.K., Rosin, P.L.: Example-based image colorization using locality consistent sparse representation. IEEE Trans. Image Process. 26(11), 5188–5202 (2017). https://doi.org/10.1109/TIP.2017.2732239

    Article  MathSciNet  MATH  Google Scholar 

  27. Arbelot, B., Vergne, R., Hurtut, T., Thollot, J.: Local texture-based color transfer and colorization. Comput. Graph. 62, 15–27 (2017). https://doi.org/10.1016/j.cag.2016.12.005

    Article  Google Scholar 

  28. **a, Y., Qu, S., Wan, S.: Scene guided colorization using neural networks. Neural Comput. Appl. 0123456789 (2018). https://doi.org/10.1007/s00521-018-3828-z

  29. Su, Z., Liang, X., Guo, J., Gao, C., Luo, X.: An edge-refined vectorized deep colorization model for grayscale-to-color images. Neurocomputing 311, 305–315 (2018). https://doi.org/10.1016/j.neucom.2018.05.082

    Article  Google Scholar 

  30. Joshi, M.R., Nkenyereye, L., Joshi, G.P., Riazul Islam, S.M., Abdullah-Al-wadud, M., Shrestha, S.: Auto-colorization of historical images using deep convolutional neural networks. Mathematics 8(12), 1–17 (2020). https://doi.org/10.3390/math8122258

  31. Wan, S., **a, Y., Qi, L., Yang, Y.H., Atiquzzaman, M.: Automated colorization of a grayscale image with seed points propagation. IEEE Trans. Multimed. 22(7), 1756–1768 (2020). https://doi.org/10.1109/TMM.2020.2976573

    Article  Google Scholar 

  32. Pahal, S., Sehrawat, P.: Image colorization with deep convolutional neural networks. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds.) Advances in Communication and Computational Technology. LNEE, vol. 668, pp. 45–56. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5341-7_4

    Chapter  Google Scholar 

  33. Thawonmas, R., Nguyen, T., Mori, K.: Image colorization using a deep convolutional neural network, p. 2 (2016)

    Google Scholar 

  34. Zhao, Y., Xu, D., Zhang, Y.: Image colorization using convolutional neural network. In: Tan, T., et al. (eds.) IGTA 2016. CCIS, vol. 634, pp. 238–244. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2260-9_27

    Chapter  Google Scholar 

  35. Dabas, C., Jain, S., Bansal, A., Sharma, V.: Implementation of image colorization with convolutional neural network. Int. J. Syst. Assur. Eng. Manage. 11(3), 625–634 (2020). https://doi.org/10.1007/s13198-020-00960-5

    Article  Google Scholar 

  36. 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). https://doi.org/10.1109/ACCESS.2020.3040737

    Article  Google Scholar 

  37. Zhang, L.M., et al.: Two-stage sketch colorization. In: SIGGRAPH Asia 2018 Technical Paper SIGGRAPH Asia 2018, vol. 37, no. 6, December 2018. https://doi.org/10.1145/3272127.3275090

  38. Baldassarre, F., et al.: Deep koalarization: image colorization using CNNs and inception-resnet-v2. ar**v, no. June 2017, pp. 1–12 (2017)

    Google Scholar 

  39. Zhou, Y., Hwang, J.: Image colorization with deep convolutional neural networks (2016)

    Google Scholar 

  40. He, M., Chen, D., Liao, J., Sander, P.V., Yuan, L., Kong, H.: Deep exemplar-based colorization. ACM Trans. Graph. 37(4) (2018). https://doi.org/10.1145/3197517.3201365

  41. Cheng, Z., et al.: Deep colorization (2016)

    Google Scholar 

  42. Zhang, W., Fang, C.-W., Li, G.-B.: Automatic colorization with improved spatial coherence and boundary localization. J. Comput. Sci. Technol. 32(3), 494–506 (2017). https://doi.org/10.1007/s11390-017-1739-6

    Article  Google Scholar 

  43. Kang, S., Chang, J., Choo, J., Chang, J.: Consistent comic colorization with pixel-wise background classification, vol. 1, no. Nips, pp. 1–6 (2017)

    Google Scholar 

  44. **ao, Y., Zhou, P., Zheng, Y.: Interactive deep colorization using simultaneous global and local inputs, no. 4, pp. 1887–1891 (2019)

    Google Scholar 

  45. Limmer, M., Lensch, H.P.A.A.: Infrared colorization using deep convolutional neural networks. In: Proceedings of 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, pp. 61–68 (2017). https://doi.org/10.1109/ICMLA.2016.114

  46. Mouzon, T., Pierre, F., Berger, M.-O.: Joint CNN and variational model for fully-automatic image colorization. In: Lellmann, J., Burger, M., Modersitzki, J. (eds.) SSVM 2019. LNCS, vol. 11603, pp. 535–546. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22368-7_42

    Chapter  Google Scholar 

  47. Cheng, Z., Yang, Q., Sheng, B.: Colorization using neural network ensemble. IEEE Trans. Image Process. 26(11), 5491–5505 (2017). https://doi.org/10.1109/TIP.2017.2740620

    Article  MathSciNet  MATH  Google Scholar 

  48. Tang, C., Zheng, X., Zhu, W.: A fast near-infrared image colorization deep learning mode, pp. 118–130 (2018)

    Google Scholar 

  49. Guadarrama, S., et al.: Pixcolor: pixel recursive colorization. ar**v, pp. 1–17 (2017)

    Google Scholar 

  50. Zhang, R., et al.: Real-time user-guided image colorization with learned deep priors. ACM Trans. Graph. 36(4) (2017). https://doi.org/10.1145/3072959.3073703

  51. Manjunatha, V., Iyyer, M., Boyd-Graber, J., Davis, L.: Learning to color from language. In: NAACL HLT 2018 - 2018 Conference on North American Chapter of the Association for Computational Linguistics: Human Language Technologies- Proceedings Conference, vol. 2, pp. 764–769 (2018). https://doi.org/10.18653/v1/n18-2120

  52. Royer, A., Kolesnikov, A., Lampert, C.H.: Probabilistic image colorization. ar**v, pp. 1–15 (2017)

    Google Scholar 

  53. Daly, R.: CNN assisted colorization of grayscale images. Cs231N.Stanford.Edu (2016)

    Google Scholar 

  54. Varga, D., Sziranyi, T.: Fully automatic image colorization based on convolutional neural network, pp. 3691–3696 (2016). https://doi.org/10.1109/ICPR.2016.7900208

  55. Zhao, J., Liu, L., Snoek, C.G.M.M., Han, J., Shao, L.: Pixel-level semantics guided image colorization. ar**v, pp. 1–12 (2018)

    Google Scholar 

  56. Fenu, S., Bagwell, C.: Image colorization using residual networks, pp. 1–8 (2016). https://www.cc.gatech.edu/~hays/7476/projects/Stefano_Carden.pdf

  57. Bagaria, V.K.: CS231N project: coloring black and white world using deep neural nets. Cs231N.Stanford.Edu (2016)

    Google Scholar 

  58. Han, L.M.G.: Combining deep convolutional neural networks with Markov random fields for image colorization, p. 281 (2016)

    Google Scholar 

  59. He, M., Liao, J., Chen, D., Yuan, L., Sander, P.V.: Progressive color transfer with dense semantic correspondences. ar**v (2018)

    Google Scholar 

  60. Liang, X., Su, Z., **ao, Y., Guo, J., Luo, X.: Deep patch-wise colorization model for grayscale images. In: SA 2016 - SIGGRAPH ASIA 2016 Technical Briefs (2016). https://doi.org/10.1145/3005358.3005375

  61. Cheng, Z.: Deep colorization.pdf, vol. 1, pp. 415–423 (2015)

    Google Scholar 

  62. Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 577–593. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_35

    Chapter  Google Scholar 

  63. Iizuka, S., Simo-Serra, E., 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. 35(4), 1–11 (2016). https://doi.org/10.1145/2897824.2925974

    Article  Google Scholar 

  64. Su, J.-W., Chu, H.-K., Huang, J.-B.: Instance-aware image colorization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2020, pp. 7968–7977 (2020). https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.html

  65. Tran, T.-B., Tran, T.-S.: Automatic natural image colorization. In: Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds.) ACIIDS 2020. LNCS (LNAI), vol. 12033, pp. 612–621. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41964-6_53

    Chapter  Google Scholar 

  66. Ozbulak, G.: Image colorization by capsule networks. In: IEEE Computing Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, pp. 2150–2158 (2019). https://doi.org/10.1109/CVPRW.2019.00268

  67. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_40

    Chapter  Google Scholar 

  68. Vitoria, P., Raad, L., Ballester, C.: ChromaGAN: adversarial picture colorization with semantic class distribution. In: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp. 2434–2443, July 2020. https://doi.org/10.1109/WACV45572.2020.9093389

  69. Zhao, J., Han, J., Shao, L., Snoek, C.G.M.: Pixelated semantic colorization. Int. J. Comput. Vis. 128(4), 818–834 (2019). https://doi.org/10.1007/s11263-019-01271-4

    Article  Google Scholar 

  70. Suárez, P.L., Sappa, A.D., Vintimilla, B.X.: Learning to colorize infrared images. In: De la Prieta, F., et al. (eds.) PAAMS 2017. Advances in Intelligent Systems and Computing, vol. 619, pp. 164–172. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_16

    Chapter  Google Scholar 

  71. Kong, G., Tian, H., Duan, X., Long, H.: Adversarial edge-aware image colorization with semantic segmentation. IEEE Access 9, 28194–28203 (2021). https://doi.org/10.1109/ACCESS.2021.3056144

    Article  Google Scholar 

  72. Bahng, H., et al.: Coloring with words: guiding image colorization through text-based palette generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 443–459. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01258-8_27

    Chapter  Google Scholar 

  73. Liu, Y., Qin, Z., Wan, T., Luo, Z.: Auto-painter: cartoon image generation from sketch by using conditional Wasserstein generative adversarial networks. Neurocomputing 311, 78–87 (2018). https://doi.org/10.1016/j.neucom.2018.05.045

    Article  Google Scholar 

  74. Suarez, P.L., Sappa, A.D., Vintimilla, B.X.: Infrared image colorization based on a triplet DCGAN architecture. In: IEEE Computing Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017-July, pp. 212–217 (2017). https://doi.org/10.1109/CVPRW.2017.32

  75. Lee, J., Kim, E., Lee, Y., Kim, D., Chang, J., Choo, J.: Reference-based sketch image colorization using augmented-self reference and dense semantic correspondence. ar**v, pp. 5801–5810 (2020)

    Google Scholar 

  76. Cao, Y., Zhou, Z., Zhang, W., Yu, Y.: Unsupervised diverse colorization via generative adversarial networks. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 151–166. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_10

    Chapter  Google Scholar 

  77. Zou, C., Mo, H., Gao, C., Du, R., Fu, H.: Language-based colorization of scene sketches. ACM Trans. Graph. 38(6) (2019). https://doi.org/10.1145/3355089.3356561

  78. Frans, K.: Outline Colorization through tandem adversarial networks. ar**v (2017)

    Google Scholar 

  79. Hensman, P., Aizawa, K.: CGAN-based manga colorization using a single training image. In: Proceedings of International Conference on Document Analysis and Recognition, ICDAR, vol. 3, pp. 72–77 (2018). https://doi.org/10.1109/ICDAR.2017.295

  80. Koo, S.: Automatic colorization with deep convolutional generative adversarial networks. Cs231N.Stanford.Edu, no. Figure 1 (2016)

    Google Scholar 

  81. Yoo, S., Bahng, H., Chung, S., Lee, J., Chang, J., Choo, J.: Coloring with limited data: few-shot colorization via memory augmented networks. In: Proceedings of IEEE Computing Society Conference on Computer Vision and Pattern Recognit., vol. 2019-June, pp. 11275–11284 (2019). https://doi.org/10.1109/CVPR.2019.01154

  82. Zhao, Y., Po, L. M., Cheung, K.W., Yu, W.Y., Rehman, Y.A.U.: SCGAN: saliency map-guided colorization with generative adversarial network. IEEE Trans. Circuits Syst. Video Technol. 1–17 (2020). https://doi.org/10.1109/TCSVT.2020.3037688

  83. Kataoka, Y., Matsubara, T., Uehara, K.: Automatic manga colorization with color style by generative adversarial nets. In: Proc. - 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017, pp. 495–499 (2017). https://doi.org/10.1109/SNPD.2017.8022768

  84. Johari, M.M., Behroozi, H.: Grayscale image colorization using cycle-consistent generative adversarial networks with residual structure enhancer.In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, vol. 2020-May, pp. 2223–2227 (2020). https://doi.org/10.1109/ICASSP40776.2020.9054432

  85. Kumar, M., Weissenborn, D., Kalchbrenner, N.: Colorization transformer, pp. 1–24 (2021)

    Google Scholar 

  86. Halder, S.S., De, K., Roy, P.P.: Perceptual conditional generative adversarial networks for end-to-end image colourization. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11362, pp. 269–283. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20890-5_18

    Chapter  Google Scholar 

  87. Dhir, R., Ashok, M., Gite, S., Kotecha, K.: Automatic image colorization using GANs. In: Patel, K.K., Garg, D., Patel, A., Lingras, P. (eds.) icSoftComp 2020. CCIS, vol. 1374, pp. 15–26. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0708-0_2

    Chapter  Google Scholar 

  88. Huang, S., et al.: A fully-automatic image colorization scheme using improved CycleGAN with skip connections. Multimed. Tools Appl. 80(17), 26465–26492 (2021). https://doi.org/10.1007/s11042-021-10881-5

    Article  Google Scholar 

  89. Wu, M., et al.: Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space. Vis. Comput. 37(7), 1707–1729 (2020). https://doi.org/10.1007/s00371-020-01933-2

    Article  Google Scholar 

  90. Ci, Y., Ma, X., Wang, Z., Li, H., Luo, Z.: User-guided deep anime line art colorization with conditional adversarial networks. In: MM 2018 – Proceedings of 2018 ACM Multimedia Conference, pp. 1536–1544 (2018). https://doi.org/10.1145/3240508.3240661

  91. Kataoka, Y., Mastubara, T., Uehara, K.: Deep manga colorization with color style extraction by conditional adversarially learned inference. Inf. Eng. Express 3(4), 55–66 (2017)

    Article  Google Scholar 

  92. Kiani, L., Saeed, M., Nezamabadi-pour, H.: Image colorization using generative adversarial networks and transfer learning. In: Iran Conference on Machine Vision and Image Processing MVIP, vol. 2020-Febru, February 2020. https://doi.org/10.1109/MVIP49855.2020.9116882

  93. Hicsonmez, S., Samet, N., Akbas, E., Duygulu, P.: Adversarial segmentation loss for sketch colorization, no. Section 4, February 2021

    Google Scholar 

  94. Li, F., Ma, L., Cai, J.: Multi-discriminator generative adversarial network for high resolution grayscale satellite image colorization. In: International Geoscience and Remote Sensing Symposium, vol. 2018-July, pp. 3489–3492 (2018). https://doi.org/10.1109/IGARSS.2018.8517930

  95. Nazeri, K., Ng, E., Ebrahimi, M.: Image colorization using generative adversarial networks. In: Perales, F.J., Kittler, J. (eds.) AMDO 2018. LNCS, vol. 10945, pp. 85–94. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94544-6_9

    Chapter  Google Scholar 

  96. Ji, G., Wang, Z., Zhou, L., **a, Y., Zhong, S., Gong, S.: SAR image colorization using multidomain cycle-consistency generative adversarial network. IEEE Geosci. Remote Sens. Lett. 18(2), 296–300 (2021). https://doi.org/10.1109/LGRS.2020.2969891

    Article  Google Scholar 

  97. Deshpande, A., Lu, J., Yeh, M.-C. C., Chong, M. J., Forsyth, D.: Learning diverse image colorization. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, no. Section 4, pp. 2877–2885 (2017). https://doi.org/10.1109/CVPR.2017.307

  98. Messaoud, S., Forsyth, D., Schwing, A.G.: Structural consistency and controllability for diverse colorization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 603–619. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_37

    Chapter  Google Scholar 

  99. Su, J.W., Chu, H.K., Bin Huang, J.: Instance-aware Image Colorization. ar**v (2020)

    Google Scholar 

  100. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2018). https://doi.org/10.1109/TPAMI.2017.2723009

    Article  Google Scholar 

  101. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  102. **ao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: SUN database: large-scale scene recognition from abbey to zoo. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3485–3492 (2010). https://doi.org/10.1109/CVPR.2010.5539970

  103. Patterson, G., Hays, J.: SUN attribute database: Discovering, annotating, and recognizing scene attributes. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2751–2758 (2012). https://doi.org/10.1109/CVPR.2012.6247998

  104. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)

    Google Scholar 

  105. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  106. Caesar, H., Uijlings, J., Ferrari, V.: COCO-stuff: thing and stuff classes in context (2018)

    Google Scholar 

  107. Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98–136 (2014). https://doi.org/10.1007/s11263-014-0733-5

    Article  Google Scholar 

  108. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset (2017)

    Google Scholar 

  109. Zhou, B., et al.: Semantic understanding of scenes through the ADE20K dataset. Int. J. Comput. Vis. 127(3), 302–321 (2018). https://doi.org/10.1007/s11263-018-1140-0

    Article  Google Scholar 

  110. Khosla, A, et al.: Understanding and predicting image memorability at a large scale. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 Inter, pp. 2390–2398 (2015). https://doi.org/10.1109/ICCV.2015.275

  111. Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Kawulok, M., Celebi, M.E., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 189–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25958-1_8

    Chapter  Google Scholar 

  112. Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., **ao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop, June 2015.

    Google Scholar 

  113. Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, vol. 2017-July, pp. 126–135 (2017). https://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/html/Agustsson_NTIRE_2017_Challenge_CVPR_2017_paper.html

  114. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  115. Wang, S., Ma, K., Yeganeh, H., Wang, Z., Lin, W.: A patch-structure representation method for quality assessment of contrast changed images. IEEE Signal Process. Lett. 22(12), 2387–2390 (2015). https://doi.org/10.1109/LSP.2015.2487369

    Article  Google Scholar 

  116. Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41(3), 541–551 (2016). https://doi.org/10.1109/JOE.2015.2469915

    Article  Google Scholar 

  117. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068

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  1. Department of Computer Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    M. H. Noaman, H. Khaled & H. M. Faheem

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  1. M. H. Noaman
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  2. H. Khaled
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  3. H. M. Faheem
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Correspondence to M. H. Noaman .

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  1. Information Technology Department, Cairo University, Faculty of Computers and Artificial Intelligence, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VÅ B-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. Fujian University of Technology, Shulin District, New Taipei, Taiwan

    Kuo-Chi Chang

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. University of Salford, Manchester, UK

    Tarek Gaber

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Noaman, M.H., Khaled, H., Faheem, H.M. (2022). Image Colorization: A Survey of Methodolgies and Techniques. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_11

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