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

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 localdependent 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. 