Leveraging Frequency and Spatial Domain Information for Underwater Image Restoration

Abstract

Abstract Underwater image restoration is pivotal for enhancing the visual perception of underwater robots by improving image visibility and quality. Typically, underwater images are compromised by color distortion, low contrast, and noise, which degrade the robots’ environmental perception. This paper introduces FS-Net, an innovative approach that synergistically utilizes information from both the frequency and spatial domains to overcome these challenges. By strategically separating and processing different frequency components, FS-Net effectively reduces noise and preserves intricate details. Concurrently, advanced spatial domain filtering and enhancement techniques are employed to augment contrast and rectify color distortions. This dual-domain integration ensures a comprehensive and efficient restoration process. We rigorously evaluate FS-Net using a well-established underwater image dataset, where it demonstrates substantial enhancements in both visual quality and quantitative performance metrics over existing methods. This work significantly advances underwater imaging technology, offering a robust solution that markedly improves image clarity and utility for underwater robotic applications.