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CPDM: Content-preserving diffusion model for underwater image enhancement.

Xiaowen Shi¹, Yuan-Gen Wang²

¹Guangzhou University, School of Computer Science and Cyber Engineering, Guangzhou, 510006, China.

Scientific Reports

|December 29, 2024

Summary

This summary is machine-generated.

This study introduces a Content-Preserving Diffusion Model (CPDM) for stable underwater image enhancement. CPDM adapts diffusion models to improve image quality despite changing underwater conditions.

Keywords:

Content-preserving Diffusion model Underwater image enhancement

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Underwater image enhancement (UIE) faces challenges due to complex and variable aquatic environments.
Current methods, relying on physical models or data-driven approaches, struggle with performance bottlenecks caused by changing conditions and training instability.

Purpose of the Study:

To adapt diffusion models for robust underwater image enhancement.
To propose a Content-Preserving Diffusion Model (CPDM) that addresses performance limitations of existing UIE methods.

Main Methods:

Leveraging a diffusion model for stable training.
Designing a content-preserving framework with a conditional input module using raw and difference images.
Implementing a content compensation module to extract low-level features for content-aware training.

Main Results:

CPDM demonstrated superior performance on LSUI, UIEB, and EUVP datasets.
The model achieved better subjective and objective metrics compared to state-of-the-art methods.
Outperformed existing methods in overall performance for underwater image enhancement.

Conclusions:

The proposed CPDM effectively enhances underwater images by adapting diffusion models.
CPDM offers improved adaptability and content preservation for UIE tasks.
The method provides a stable and high-performing solution for underwater image enhancement challenges.