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QuadPrior++: Multi-Dimension Augmented Physical Prior for Zero-Reference Illumination Enhancement.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 26, 2025
Summary
This study introduces a novel framework for low-light image enhancement using generative diffusion models and an illumination-invariant prior. It achieves zero-shot enhancement without specific training data, improving generalization and efficiency.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Current low-light enhancement methods struggle with generalization due to scene dependency and inadequate modeling of natural image priors.
- Existing approaches often rely on supervised or self-supervised learning, limiting their adaptability to diverse lighting conditions.
Purpose of the Study:
- To develop a zero-reference low-light enhancement framework that overcomes the limitations of existing methods.
- To leverage generative diffusion models and a novel illumination-invariant prior for robust image enhancement.
- To create a computationally efficient and practical solution for real-world low-light imaging challenges.
Main Methods:
- Proposed a novel illumination-invariant prior derived from physical light transfer theory to bridge normal and low-light domains.
- Developed a prior-to-image restoration framework using generative diffusion models pre-trained on normal-light data.
- Introduced a prior-injected distillation paradigm to create compact CNN-based networks from diffusion models, incorporating multi-domain regularization.
Main Results:
- Achieved zero-shot low-light enhancement without requiring low-light-specific training data, demonstrating superior generalization.
- The distilled CNN models maintained high fidelity and perceptual quality while significantly reducing computational costs.
- The framework successfully handled over-exposure scenarios, showcasing versatility across complex lighting conditions.
Conclusions:
- The proposed framework offers a robust and efficient solution for low-light image enhancement, outperforming existing methods in generalization and adaptability.
- The illumination-invariant prior provides a powerful tool for zero-shot learning in image restoration tasks.
- The distillation approach makes advanced generative model capabilities practical for real-world applications with reduced computational demands.