Temel Kavramlar
Proposing a zero-reference low-light enhancement framework using physical quadruple priors to achieve superior performance in various scenarios.
Özet
The content introduces a novel zero-reference low-light enhancement framework that leverages physical quadruple priors derived from the Kubelka-Munk theory. The framework is trainable solely with normal light images and demonstrates robustness, interpretability, and efficiency in enhancing low-light images without the need for specific low-light data or illumination-relevant hyper-parameters. The content is structured as follows:
- Introduction to the challenge of restoring images in low-light conditions.
- Overview of supervised, unsupervised, and zero-reference methods for low-light enhancement.
- Detailed explanation of the proposed zero-reference method utilizing physical quadruple priors.
- Discussion on the learnable illumination-invariant prior and its components.
- Description of the prior-to-image mapping framework using generative diffusion models.
- Experiments conducted to benchmark the proposed method against existing techniques.
- Ablation studies analyzing the impact of different design elements on performance.
- Conclusion highlighting the efficiency and effectiveness of the proposed lightweight model.
İstatistikler
"Extensive experiments demonstrate our framework’s superiority in various scenarios."
"Our lightweight version maintains comparable performance while significantly improving inference speed."
Alıntılar
"Our model exhibits superior performance in various under-lit scenarios without relying on any specific low-light data."
"Our approach combines interpretability, robustness, and efficiency."