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One-to-one Example-based Automatic Image Coloring Using Deep Convolutional Generative Adversarial Network

Junghoon Seo 1, Taewon Yoon 1, Jinwoo Kim 1, and Kin Choong Yow 2
1. Electrical Engineering and Computer Science Department, GIST College, Gwangju, Republic of Korea
2. Division of Liberal Arts and Sciences, GIST College, Gwangju, Republic of Korea
Abstract—Due to the indeterminate nature of the problem, image colorization techniques currently rely heavily on human intuition. Using deep convolutional networks, we can build a system that takes a source image to guide local-dependent feature color mapping and color a grayscale target image. Unlike most other convolutional neural network approaches that require a lot of training data, our proposed system uses only one image for training for each target image. Our system is based on deep convolutional generative adversarial networks, which contains concepts of both supervised and unsupervised learning. We proposed a model architecture, objective functions, and both preprocessing and postprocessing algorithms for the image coloring process. We evaluated our system on a variety of input images and showed that it produce excellent results. 

Index Terms—automatic image coloring, deep convolutional generative adversarial network, image processing, computer vision, neural network

Cite: Junghoon Seo, Taewon Yoon, Jinwoo Kim, and Kin Choong Yow, "One-to-one Example-based Automatic Image Coloring Using Deep Convolutional Generative Adversarial Network," Vol. 8, No. 2, pp. 80-85, May, 2017. doi: 10.12720/jait.8.2.80-85
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