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ZeroPIE: Zero-Reference Polarization-Aware Low-Light Enhancement via Physically Derived Illumination-Invariant Priors
Summary
This study introduces ZeroPIE, a novel zero-reference framework for enhancing low-light polarization images. It uses illumination-invariant priors to reconstruct normal-light polarization data without low-light training, improving performance in challenging conditions.
Area of Science:
- Computer Vision
- Optical Imaging
Background:
- Color polarization cameras offer rich data but degrade in low light.
- Current polarization-aware low-light enhancement (PLLE) methods rely on supervised learning, limiting generalization.
Purpose of the Study:
- To develop a robust, zero-reference framework for PLLE that overcomes limitations of supervised methods.
- To enable reliable polarization imaging in low-light conditions without specialized training data.
Main Methods:
- Proposed ZeroPIE, a framework trained only on normal-light polarization images.
- Derived four physically grounded, illumination-invariant priors: Degree of Linear Polarization (DoLP), Angle of Linear Polarization (AoLP), geometric invariant, and chromatic invariant.
- Utilized a Residual Denoising Diffusion Model (RDDM) guided by these priors for image reconstruction.
Main Results:
- ZeroPIE demonstrates superior performance in radiance restoration and polarization parameter reconstruction compared to state-of-the-art methods.
- The framework shows strong robustness to unknown real-world degradations.
- Achieved significant improvements in downstream vision tasks.
Conclusions:
- ZeroPIE offers a novel, data-efficient approach to PLLE by leveraging physical priors.
- The method effectively bridges the gap between low-light and normal-light polarization images without requiring low-light training data.
- Presents a significant advancement for polarization imaging applications in challenging environments.
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