Abstract
Colorization, a task of coloring monochrome images or videos, plays an important role in the human perception of visual information, to black and white pictures or videos. Colorizing, when done manually in Photoshop, a single picture might take months to get exactly correct. Understanding the tediousness of the task and inspired by the benefits of artificial intelligence, we propose a mechanism to automate the coloring process with the help of convolutional neural networks (CNNs). Firstly, an Alpha version is developed which successfully works on trained images but fails to colorize images, and the network has never seen before. Subsequently, a Beta version is implemented which is able to overcome the limitations of Alpha version and works well for untrained images. To further enhance the network, we fused the deep CNN with a classifier called Inception ResNet V2 which is a pre-trained model. Finally, the training results are observed for all the versions followed by a comparative analysis for trained and untrained images.
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References
Burns G, Colorization. Museum of broadcast communications: The Encyclopedia of Television. http://www.museum.tv/archives/etv/index.html
Welsh T, Ashikhmin M, Mueller K (2002) Transferring color to greyscale images. ACM Trans Graph 21(3):277–280
Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. In: Proceedings ACM SIGGRAPH conference, pp 689–694
Yatziv L, Sapiro G (2006) Fast image and video colorization using chrominance blending. Trans Img Proc 15(5):1120–1129
Deshpande A, Rock J, Forsyth D (2015) Learning large-scale automatic image colorization. In: Proceedings of the IEEE international conference on computer vision, pp 567–575
Chia AYS, Zhuo S, Gupta RK, Tai YW, Cho SY, Tan P, Lin S (2011) Semantic colorization with internet images. In: TOG, vol. 30 no 6, ACM, p 156
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, Springer, pp 818–833
Iizuka S, Simo-Serra E, Ishikawa H (2016) 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
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI, vol. 4, p 12
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
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Pahal, S., Sehrawat, P. (2021). Image Colorization with Deep Convolutional Neural Networks. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_4
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DOI: https://doi.org/10.1007/978-981-15-5341-7_4
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