TURBIT: Generating Turbid Underwater Images with Diffusion and Differential Transformers

Underwater vision in muddy and turbid conditions poses significant challenges for underwater exploration and aquatic research. Existing benchmark datasets focus primarily on oceanic water scenes, limiting their applicability to highly turbid environments. This paper proposes a two-stage framework that combines a diffusion transformer (M1) and a differential attention model (M2) to generate synthetic turbid underwater datasets. M1, trained on the ImageNet Underwater Subset, acts as a foundational content generator, while M2, trained using a few-shot approach on a curated dataset, models turbidity-specific context. A conditional fusion module integrates both models, ensuring realism while preserving structural integrity. The plug-and-play design enables domain-specific fine-tuning of M2, enhancing adaptability across diverse conditions. We benchmarked the generated turbid underwater datasets against state-of-the-art underwater image enhancement (UIE) methods, with extensive experiments on fusion strategies and depth preservation validating their effectiveness. This framework establishes a scalable data generation pipeline that advances UIE research in challenging underwater scenarios. Codes and datasets are available at https://github.com/Alik033/TURBIT.