Near-Infrared Image Colorization Using Unsupervised Contrastive Learning

Near-Infrared (NIR) images are widely used in a variety of low-light situations for security and safety applications. A colorised version of NIR images provide better image understanding and interpretation of features. Because the number of NIR-RGB paired datasets is limited and often unavailable, a method to convert a given NIR image to an RGB image is highly desirable. The present work proposes an unsupervised image to image translation technique for generating colorized images (UGCI) for transforming an input NIR image to an RGB image. UGCI outperforms present NIR-RGB colorizing models and have shown approximately 57% improvement in terms of Fréchet inception distance (FID) with reduced training time and less memory usage. Finally, a thorough comparative study based on different datasets is carried out to confirm superiority over leading colorization approaches in qualitative and quantitative assessments.