Single-Step Latent Diffusion for Underwater Image Restoration.

Area of Science:

• Computer Vision
• Image Processing
• Marine Technology

Background:

• Underwater images suffer from color loss, low contrast, and reduced visibility due to light attenuation and scattering.
• Existing diffusion-based restoration methods are computationally expensive and produce artifacts in complex underwater scenes.
• Accurate restoration is crucial for marine ecology, aquaculture, construction, and archaeology.

Purpose of the Study:

• To develop a computationally efficient and effective underwater image restoration algorithm for complex scenes.
• To address the limitations of existing pixel-domain diffusion-based methods in handling depth variation and geometric complexity.
• To enhance the visual quality and accuracy of underwater imagery for various applications.

Main Methods:

• Introduced SLURPP, a novel network architecture combining latent diffusion models with explicit scene decomposition.
• Developed a physics-based synthetic data generation pipeline for realistic underwater image degradation.
• Trained SLURPP using diverse, annotated synthetic data capturing varied underwater conditions.

Main Results:

• SLURPP achieves state-of-the-art performance on synthetic and real-world benchmarks.
• Demonstrated over 200x speed improvement compared to existing diffusion-based methods.
• Achieved approximately 3 dB PSNR improvement on synthetic benchmarks and compelling qualitative results on real data.

Conclusions:

• SLURPP offers a significant advancement in underwater image restoration, balancing speed and quality.
• The novel architecture and synthetic data generation pipeline effectively handle complex underwater environments.
• This method provides a powerful tool for improving underwater imaging across scientific and industrial applications.