Presentation
Self-supervised Underwater Color Restoration via Wavelet-Diffusion Model with Filtered Multi-Scale Feature Distillation
DescriptionExisting underwater image processing methods often struggle due to the limited availability of real paired training data. Models trained on public datasets frequently fail to generalize across diverse underwater conditions and produce suboptimal color restoration. To address these challenges, we propose a self-supervised underwater color restoration framework based on a Wavelet-Diffusion Model with Filtered Multi-Scale Feature Distillation. Specifically, we introduce a wavelet-diffusion training paradigm on terrestrial images, guided by a stochastic underwater imaging model prior. This randomized control enables the model to learn diverse underwater imaging processes, facilitating effective generalization to real-world underwater images and achieving precise color restoration. Furthermore, to tackle feature entanglement in zero-shot domain generalization and mitigate the slow sampling and partial corruption issues of diffusion models, We integrate a Mamba-based U-shaped student network for multi-scale feature distillation. Additionally, we introduce a filtering mechanism to refine the diffusion sampled features, allowing the student model to outperform the teacher in both performance and image quality. Extensive experiments across multiple underwater datasets demonstrate that our approach effectively restores natural colors, eliminating water-induced distortions while achieving state-of-the-art performance in both qualitative and quantitative evaluations. Code and data for this paper are at https://github.com/zx826/FMFD
Event Type
Technical Papers
TimeTuesday, 16 December 20251:53pm - 2:04pm HKT
LocationMeeting Room S423+S424, Level 4