Abstract
Huge amount of work is getting done on Image colorization worldwide. This research paper proposes a model for image colorization while making use of fully automatic Convolutional Neural Network. Image colorization processes a daunting task, and this research paper proposes a relevant model for the prediction of A and B models for LAB color space and it makes a direct use the lightness channel. In this work, a pre-trained VGG-16 network was used for semantically interpreting the concepts associated with images and coloring the images. In the proposed work, the convolutional layer has been fused with the max pooling layer (higher one) of the VGG network. Architecture of the proposed model has been presented. The experimentation has been carried out with varying objective functions. LaMem experimental dataset has been used in this work in order to validate the proposed model. The proposed model is evaluated and results are visualized by histograms for true and predicted images for RGB values. Further, the proposed model has been compared with the existing models and performs better in terms of execution times (in s) for different image sizes and the results are tabulated.
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References
Ahmad M, Doja MN, Beg MS (2018) Security analysis and enhancements of an image cryptosystem based on hyperchaotic system. J King Saud Univ-Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.02.002
Bala A, Kaur T (2016) Local texton XOR patterns: a new feature descriptor for content-based image retrieval. Eng Sci Technol Int J 19(1):101–112
Bugeau A, Ta VT (2012) Patch-based image colorization. In: 21st international conference on pattern recognition (ICPR), IEEE, pp 3058–3061
Charpiat G, Hofmann M, Schölkopf B (2008). Automatic image colorization via multimodal predictions. In: European conference on computer vision, Springer, Berlin, pp 126–139
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Cheng Z, Yang Q, Sheng B (2015) Deep colorization. In: Proceedings of the IEEE international conference on computer vision, pp 415–423
Cireşan D, Meier U (2015) Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International joint conference on neural networks (IJCNN), IEEE, pp 1–6
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115
Huang YC, Tung YS, Chen JC, Wang SW, Wu JL (2005) An adaptive edge detection based colorization algorithm and its applications. In: Proceedings of the 13th annual ACM international conference on multimedia, ACM, pp 351–354
Hwang J, Zhou Y (2016) Image colorization with deep convolutional neural networks. In Stanford University, Technical Report
Jiang, H., Tang, S., Li, Y., Ai, D., Song, H., & Yang, J. (2019). Endoscopic image colorization using convolutional neural network. In: 2019 IEEE 7th international conference on bioinformatics and computational biology (ICBCB), IEEE, pp 162–166
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Jordan MI, LeCun Y, Solla SA (eds) Advances in neural information processing systems. MIT Press, Cambridge, pp 1097–1105
Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. In: ACM transactions on graphics (tog), ACM, vol 23, no 3, pp 689–694
Margulis D (2005) Photoshop LAB color: The canyon conundrum and other adventures in the most powerful colorspace. Peachpit Press, Berkeley
Qu Y, Pang WM, Wong TT, Heng PA (2008) Richness-preserving manga screening. In ACM transactions on graphics (TOG), ACM, vol 27, no 5, p 155
Quan W, Wang K, Yan DM, Pellerin D, Zhang X (2019). Impact of data preparation and CNN’s first layer on performance of image forensics: a case study of detecting colorized images. In: IEEE/WIC/ACM international conference on web intelligence, ACM, vol 24800, pp 127–131
R. Dahl image colorization http://tinyclouds.org/colorize
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Welsh T, Ashikhmin M, Mueller K (2002) Transferring color to greyscale images. In: ACM transactions on graphics (TOG), ACM, vol 21, no 3, pp 277–280
Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295
Yatziv L, Sapiro G (2006) Fast image and video colorization using chrominance blending. IEEE Trans Image Process 15(5):1120–1129
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Dabas, C., Jain, S., Bansal, A. et al. Implementation of image colorization with convolutional neural network. Int J Syst Assur Eng Manag 11, 625–634 (2020). https://doi.org/10.1007/s13198-020-00960-5
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DOI: https://doi.org/10.1007/s13198-020-00960-5