Abstract
Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input. In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN). The network is trained over datasets that are publicly available such as CIFAR-10 and Places365. The results between the generative model and traditional deep neural networks are compared.
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Acknowledgments
This research was supported in part by an NSERC Discovery Grant for ME. The authors gratefully acknowledge the support of NVIDIA Corporation for donation of GPUs through its Academic Grant Program.
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Appendices
AÂ CIFAR-10 Results
See Fig. 3.
Colorization results with CIFAR10. (a) Grayscale. (b) Original Image. (c) Colorized with U-Net. (d) Colorized with GAN. (Color figure online)
BÂ Places365 Results
See Fig. 4.
Colorization results with Places365 (a) Grayscale. (b) Original Image. (c) Colorized with GAN. (Color figure online)
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Nazeri, K., Ng, E., Ebrahimi, M. (2018). Image Colorization Using Generative Adversarial Networks. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2018. Lecture Notes in Computer Science(), vol 10945. Springer, Cham. https://doi.org/10.1007/978-3-319-94544-6_9
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DOI: https://doi.org/10.1007/978-3-319-94544-6_9
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