Image Colorization with Generative Adversarial Networks
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Over the last decade, the process of automatic colorization had been studied thoroughly due to its vast application such as colorization of grayscale images and restoration of aged and/or degraded images. This problem is highly ill-posed due to the extremely large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involved images that contained a common theme throughout training, and/or required highly processed data such as semantic maps as input data. In our approach, we attempted to fully generalize this 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 of the generative model and tradition deep neural networks are compared.
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