Fusing Transferred Priors and Physics-based Decomposition for Underwater Image Enhancement

2026-06-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

A novel underwater image enhancement (UIE) method, detailed in a recent work, addresses complex degradation issues like color bias, low contrast, and blur without requiring paired noisy or true labels. This approach first divides the UIE task into global color correction, haze removal, and background noise suppression, aligning with underwater physics. It then leverages multiple types of prior knowledge from other vision tasks as cross-domain supervision for each sub-task. This transfer learning-based UIE achieves state-of-the-art (SOTA) performance, significantly outperforming benchmark methods in UIE tasks and effectively boosting downstream vision tasks. The project repository is available at https://github.com/Haru2022/P2-UIE.

Key takeaway

For Computer Vision Engineers developing underwater imaging solutions, this method offers a robust approach to overcome the challenge of limited high-quality labeled data. By integrating physics-aligned decomposition with cross-domain prior transfer, your UIE models can achieve SOTA performance and significantly improve downstream task accuracy without relying on noisy pseudo-labels. Consider adopting this transfer learning strategy to enhance your underwater image processing pipelines.

Key insights

A novel UIE method fuses transferred priors and physics-based decomposition to enhance underwater images without paired labels.

Principles

• Underwater image degradation involves color bias, low contrast, and blur.
• Noisy pseudo-labels from prior UIE methods limit model performance.
• Decomposing UIE into physics-aligned sub-tasks provides theoretical soundness.

Method

The UIE task is divided into global color correction, haze removal, and background noise suppression, applying cross-domain supervision from other vision tasks in each step.

In practice

• Enhance underwater images without requiring paired ground truth labels.
• Improve performance in downstream vision tasks.
• Utilize cross-domain priors for specific degradation types.

Topics

• Underwater Image Enhancement
• Transfer Learning
• Physics-based Decomposition
• Computer Vision
• Image Degradation
• Cross-domain Supervision

Code references

• Haru2022/P2-UIE

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.