Colorizing Near Infrared Images Through a Cyclic Adversarial Approach of Unpaired Samples

This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster, less sensitive to hyper-parameters' selection and generate high quality samples. The obtained results have been quantitatively-using standard evaluation metrics-and qualitatively evaluated showing considerable improvements with respect to the state of the art.