MTUW-GAN: A Multi-Teacher Knowledge Distillation Generative Adversarial Network for Underwater Image Enhancement

Abstract

Underwater imagery is plagued by issues such as image blurring and color distortion, which significantly impede the detection and operational capabilities of underwater robots, specifically Autonomous Underwater Vehicles (AUVs). Previous approaches to image fusion or multi-scale feature fusion based on deep learning necessitated multi-branch image preprocessing prior to merging through fusion modules. However, these methods have intricate network structures and a high demand for computational resources, rendering them unsuitable for deployment on AUVs, which have limited resources at their disposal. To tackle these challenges, we propose a multi-teacher knowledge distillation GAN for underwater image enhancement (MTUW-GAN). Our approach entails multiple teacher networks instructing student networks simultaneously, enabling them to enhance color and detail in degraded images from various perspectives, thus achieving an image-fusion-level performance. Additionally, we employ middle layer channel distillation in conjunction with the attention mechanism to extract and transfer rich middle layer feature information from the teacher model to the student model. By eliminating multiplexed branching and fusion modules, our lightweight student model can directly generate enhanced underwater images through model compression. Furthermore, we introduce a multimodal objective enhancement function to refine the overall framework training, striking a balance between a low computational effort and high-quality image enhancement. Experimental results, obtained by comparing our method with existing approaches, demonstrate the clear advantages of our proposed method in terms of visual quality, model parameters, and real-time performance. Consequently, our method serves as an effective solution for real-time underwater image enhancement, specifically tailored for deployment on AUVs.