WWE-UIE: A Wavelet & White Balance Efficient Network for Underwater Image Enhancement
This work addresses the challenge of real-time underwater image enhancement for applications like marine robotics or surveillance, though it is incremental as it builds on existing hybrid approaches.
The paper tackles the problem of underwater image enhancement by proposing WWE-UIE, a compact network that integrates adaptive white balance, wavelet-based enhancement, and gradient-aware modules to restore visibility and correct color distortions, achieving competitive restoration quality with significantly fewer parameters and FLOPs for real-time inference on resource-limited platforms.
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.