In this recipe, you will learn how to use a pre-trained deep learning model to convert a grayscale image into a plausible color version. Zhang et al. propose a fully automatic image-colorization model that produces realistically colored images given a grayscale input image. The model was practiced on over a million target color images. In the testing phase, we just need to run a forward pass on the CNN to predict the output colored image when given a grayscale input. The algorithm was evaluated using a colorization Turing test, where the human participants were asked to choose between a model-generated and a ground-truth color image (which resulted in the model successfully fooling the humans in 32% of the trials). The following diagram shows the architecture of the deep CNN:
