WWE-UIE: A Wavelet & White Balance Efficient Network for Underwater Image Enhancement

TL;DR

WWE-UIE is a lightweight, interpretable neural network that enhances underwater images by combining wavelet decomposition, white balance, and edge preservation, enabling real-time performance with high restoration quality.

Contribution

The paper introduces WWE-UIE, a novel efficient underwater image enhancement network that integrates three interpretable priors for improved performance and real-time applicability.

Findings

Achieves competitive restoration quality with fewer parameters.

Enables real-time inference on resource-limited platforms.

Component ablation confirms effectiveness of each module.

Abstract

Underwater Image Enhancement (UIE) aims to restore visibility and correct color distortions caused by wavelength-dependent absorption and scattering. Recent hybrid approaches, which couple domain priors with modern deep neural architectures, have achieved strong performance but incur high computational cost, limiting their practicality in real-time scenarios. In this work, we propose WWE-UIE, a compact and efficient enhancement network that integrates three interpretable priors. First, adaptive white balance alleviates the strong wavelength-dependent color attenuation, particularly the dominance of blue-green tones. Second, a wavelet-based enhancement block (WEB) performs multi-band decomposition, enabling the network to capture both global structures and fine textures, which are critical for underwater restoration. Third, a gradient-aware module (SGFB) leverages Sobel operators with learnable gating to explicitly preserve edge structures degraded by scattering. Extensive experiments on benchmark datasets demonstrate that WWE-UIE achieves competitive restoration quality with substantially fewer parameters and FLOPs, enabling real-time inference on resource-limited platforms. Ablation studies and visualizations further validate the contribution of each component. The source code is available at https://github.com/chingheng0808/WWE-UIE.