Multi-Band NIR-Based Low-Light Image Enhancement via Dual-Teacher Cross Attention

Abstract

Low-light images often lack visibility and information, and traditional methods of adjusting camera sensitivity and exposure time can result in visual quality degradation. In this paper, we propose a low-light enhancement method that utilizes two novel approaches to address these issues. The first approach involves using multi-band NIR (Near Infra-red) to preserve structural components, while a transformer-based cross-attention module efficiently calculates the correlation between NIR and RGB for effective fusion. The second approach involves implementing dual-teacher knowledge distillation, where normal- and mid-light teacher networks transfer low-light enhancement knowledge to the student. Our proposed method produces better color and detail restoration results than existing methods, particularly in ultra low-light environments. We also provide our own datasets for two different low-light conditions, enabling wide evaluations and ablation studies.