{"ID":2839059,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16321","arxiv_id":"2511.16321","title":"WWE-UIE: A Wavelet \u0026 White Balance Efficient Network for Underwater Image Enhancement","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2511.16321","url_pdf":"https://arxiv.org/pdf/2511.16321v1","authors":"[\"Ching-Heng Cheng\",\"Jen-Wei Lee\",\"Chia-Ming Lee\",\"Chih-Chung Hsu\"]","published":"2025-11-20T12:54:08Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":606838,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2839059,"paper_url":"https://arxiv.org/abs/2511.16321","paper_title":"WWE-UIE: A Wavelet \u0026 White Balance Efficient Network for Underwater Image Enhancement","repo_url":"https://github.com/chingheng0808/WWE-UIE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}