Dual-Teacher Color and Structure Distillation for NIR Image Colorization

Near-infrared (NIR) imaging can acquire more details and textures with less noise in low-light environments compared to RGB. As a result, it has been widely used in low-light vision scenarios such as CCTV, autonomous driving, and military applications. However, applying NIR images to the human cognitive system and computer vision algorithms is challenging due to the lack of color information. Therefore, it is essential to colorize NIR images into RGB ones. We propose a teacher-student deep network with dual-teacher knowledge distillation to better estimate the original color and structure information. Specifically, our dual-teacher network is designed to separately teach distinct knowledge, such as color and structure qualities in an image, to the student. Finally, a color guided structure (CGS) and color embedding (CE) module are applied to fuse color and structure features. Our trained model can retain color consistency and detailed structure information of objects.