DTKD-Net: Dual-Teacher Knowledge Distillation Lightweight Network for Water-Related Optics Image Enhancement

Water-related optics images are often degraded by absorption and scattering effects. Current underwater image enhancement (UIE) methods improve image quality but neglect the constraints of underwater imaging environments. To address this issue, we propose a double-teacher knowledge distilling network (DTKD-Net), which uses a dynamic teaching strategy within a dual-teacher framework to enhance knowledge distillation (KD), improving the student network’s ability to capture complex underwater features. Specifically, DTKD-Net focuses on clear-to-clear and blurry-to-clear image learning to enhance underwater images. It aims to preserve details in clear images and restore blurred ones. The dual-teacher network uses an intermediate layer with the middle layer of the student network to compute feature differences for feature guidance. The network uses a dynamic strategy where a Teacher-Sub stops guidance when its output matches the student’s, which helps with contrastive learning and improves the network’s ability to handle complex underwater scenes. Extensive experiments and visual comparisons show that DTKD-Net reduces the model size, demonstrating superior efficiency and effectiveness in enhancing underwater images.