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ZeroPIE: Zero-Reference Polarization-Aware Low-Light Enhancement via Physically Derived Illumination-Invariant Priors
Summary
ZeroPIE is a novel zero-reference framework for low-light polarization imaging enhancement. It uses physics-based priors to reconstruct normal-light polarization images without low-light training data, improving performance in challenging conditions.
Area of Science:
- Computer Vision
- Optical Imaging
- Image Processing
Background:
- Color polarization cameras provide rich data but degrade in low light.
- Existing polarization-aware low-light enhancement (PLLE) methods rely on supervised learning, limiting generalization.
Purpose of the Study:
- To introduce ZeroPIE, the first zero-reference framework for PLLE.
- To develop a robust method for enhancing polarization images under low-light conditions without requiring low-light training data.
Main Methods:
- Derivation of four physically grounded illumination-invariant priors: Degree of Linear Polarization (DoLP), Angle of Linear Polarization (AoLP), geometric invariant, and chromatic invariant.
- Development of a recovery framework using a Residual Denoising Diffusion Model (RDDM) guided by these priors.
- Training exclusively on normal-light polarization images.
Main Results:
- ZeroPIE outperforms state-of-the-art methods in radiance restoration and polarization parameter reconstruction.
- Demonstrated strong robustness to unknown real-world degradations on synthetic and real-world datasets.
- Achieved superior performance in downstream vision tasks compared to existing methods.
Conclusions:
- ZeroPIE offers a robust and generalizable solution for low-light polarization image enhancement.
- The proposed physics-based priors effectively bridge the gap between low-light and normal-light polarization images.
- This zero-reference approach significantly advances the applicability of polarization imaging in real-world, low-light scenarios.
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