Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis

TL;DR

This paper introduces a novel underwater image restoration framework that combines multi-scale detail restoration and contextual feature extraction guided by a color balance prior, supported by extensive benchmarking against existing methods.

Contribution

The paper proposes a hybrid sensing approach with a color balance prior for underwater image restoration, along with a comprehensive benchmark dataset and evaluation of multiple methods.

Findings

Our method outperforms 37 state-of-the-art approaches on various datasets.

The framework effectively restores color and details in underwater images.

Extensive benchmarking provides a standard for future comparisons.

Abstract

Underwater imaging grapples with challenges from light-water interactions, leading to color distortions and reduced clarity. In response to these challenges, we propose a novel Color Balance Prior \textbf{Guided} \textbf{Hyb}rid \textbf{Sens}e \textbf{U}nderwater \textbf{I}mage \textbf{R}estoration framework (\textbf{GuidedHybSensUIR}). This framework operates on multiple scales, employing the proposed \textbf{Detail Restorer} module to restore low-level detailed features at finer scales and utilizing the proposed \textbf{Feature Contextualizer} module to capture long-range contextual relations of high-level general features at a broader scale. The hybridization of these different scales of sensing results effectively addresses color casts and restores blurry details. In order to effectively point out the evolutionary direction for the model, we propose a novel \textbf{Color Balance Prior} as a strong guide in the feature contextualization step and as a weak guide in the final decoding phase. We construct a comprehensive benchmark using paired training data from three real-world underwater datasets and evaluate on six test sets, including three paired and three unpaired, sourced from four real-world underwater datasets. Subsequently, we tested 14 traditional and retrained 23 deep learning existing underwater image restoration methods on this benchmark, obtaining metric results for each approach. This effort aims to furnish a valuable benchmarking dataset for standard basis for comparison. The extensive experiment results demonstrate that our method outperforms 37 other state-of-the-art methods overall on various benchmark datasets and metrics, despite not achieving the best results in certain individual cases. The code and dataset are available at \href{https://github.com/CXH-Research/GuidedHybSensUIR}{https://github.com/CXH-Research/GuidedHybSensUIR}.