MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency

Abstract

Introduction The advancement of Underwater Human-Robot Interaction technology has significantly driven marine exploration, conservation, and resource utilization. However, challenges persist due to the limitations of underwater robots equipped with basic cameras, which struggle to handle complex underwater environments. This leads to blurry images, severely hindering the performance of automated systems. Methods We propose MUFFNet, an underwater image enhancement network leveraging multi-scale frequency analysis to address the challenge. The network introduces a frequency-domain-based convolutional attention mechanism to extract spatial information effectively. A Multi-Scale Enhancement Prior algorithm enhances high-frequency and low-frequency features while the Information Flow Interaction module mitigates information stratification and blockage. A Multi-Scale Joint Loss framework facilitates dynamic network optimization. Results Experimental results demonstrate that MUFFNet outperforms existing state-of-the-art models while consuming fewer computational resources and aligning enhanced images more closely with human visual perception. Discussion The enhanced images generated by MUFFNet exhibit better alignment with human visual perception, making it a promising solution for improving underwater robotic vision systems.