Multi-Band NIR Colorization via Dual-Teacher Color and Structure Distillation

Near-infrared (NIR) imaging captures more details and textures with less noise in low-light environments compared to RGB, making it widely used in such scenarios. However, the lack of color in NIR poses challenges for human cognition and computer vision, necessitating its colorization. We propose a multi-band NIR imaging approach with dual-teacher knowledge distillation to better estimate original color and structure. The dual-teacher network, with color- and structure-teacher, separately instructs the student network on color and structural qualities. To fuse these features, the Color Guided Structure (CGS) and the Color Embedding (CE) modules are applied. The CGS module enhances correlation by synchronizing color and structure under the guidance of the color feature, while the CE module effectively fuses them. Our model retains color consistency and detailed structure information of objects. The source code and datasets will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ymin2570/Multi-Band-NIR-colorization/</uri>.