Key Takeaways & Executive Findings
- •• • PDPrior operates with zero training data and zero ground-truth reflection-free images, eliminating the dataset dependency that cripples existing polarization-based methods under unseen lighting; this enables immediate deployment in video conferencing and facial recognition without costly paired data collection. • • The method achieves artifact-free, high-fidelity reflection removal on real-world eyeglass images captured by a division-of-focal-plane polarization camera across indoor and outdoor lighting, yielding higher face image quality assessment scores for recognition than state-of-the-art methods—directly reducing recognition errors in biometric authentication. • • By alternately updating reflection and transmission variables via gradient descent at each diffusion sampling step, PDPrior embeds physical interpretability through the forward model of reflection formation, ensuring robustness where purely data-driven approaches fail under complex lighting. • • The framework extends to window photography, showcase displays, and driver monitoring, providing a general reflection removal solution that bypasses the scalability bottleneck of paired dataset acquisition and lowers deployment barriers for consumer photography and intelligent vision systems.
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Abstract
Eyeglass reflection severely degrades facial feature visibility in video conferencing and facial recognition, where the captured image is a superposition of transmission and reflection layers. Existing polarization-based reflection removal methods depend on large-scale paired polarization datasets, limiting generalization to unseen lighting conditions. This work introduces PDPrior, an untrained polarization-guided diffusion prior that requires no training data and no ground-truth reflection-free images. PDPrior leverages the generative prior of a diffusion model and incorporates polarization information as guidance to control the generation process. The reflection diffusion model uses the degree of linear polarization (DoLP) to preliminarily identify reflection regions and exploits the diffusion prior of progressively darkening facial content, enabling focus on reflective areas. The transmission coefficient computed from DoLP guides transmission image generation via the physical forward model of reflection formation. During each sampling step, reflection and transmission variables are alternately updated through gradient descent based solely on the test sample, conferring adaptability to complex lighting and diverse scenes. Real-world eyeglass reflection images were collected using a division-of-focal-plane polarization camera under various indoor and outdoor lighting environments. Experimental results demonstrate that PDPrior effectively removes eyeglass reflection, producing high-fidelity face reconstructions with no visible artifacts and achieving more robust face image quality assessment scores for recognition performance. The framework generalizes to window photography, showcase displays, and driver monitoring.
1. Introduction
Video conferencing platforms such as Zoom, Microsoft Teams, Tencent Meeting, and Feishu have experienced explosive expansion, while facial recognition-based biometric authentication has become mainstream in government and finance. In these scenarios, eyeglass reflection—arising from specular highlights, ambient scene reflections, or both—occludes critical facial features, degrading visual perception and increasing recognition errors. The captured image is physically modeled as a superposition of a transmission layer and a reflection layer, making separation a blind source separation problem under unknown lighting.
Existing polarization reflection removal approaches rely on large-scale paired polarization datasets, which are expensive to acquire and fail to generalize to unseen lighting conditions. PDPrior addresses this bottleneck by requiring no training data and no ground-truth reflection-free images. It operates solely on polarization observations collected at test time, using the degree of linear polarization (DoLP) to identify reflection regions and compute transmission coefficients. A diffusion model's generative prior is guided by polarization and a self-supervised loss based on the physical forward model, with reflection and transmission variables alternately updated via gradient descent. This untrained optimization achieves artifact-free, high-fidelity reflection removal and robust face image quality assessment scores for recognition, with extensibility to window photography, showcase displays, and driver monitoring.
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Yating CHEN, Liangcai CAO (2026). Polarization-guided diffusion prior for eyeglass reflection removal. Opto-Electronic Advances (光电进展). https://doi.org/10.29026/oea.2026.250249
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Frequently Asked Questions
What is the failure mechanism of PDPrior under extreme lighting conditions, such as strong specular highlights or low-light outdoor scenes?
PDPrior relies on DoLP to preliminarily identify reflection regions and compute transmission coefficients. Under strong specular highlights, DoLP may saturate, causing inaccurate reflection masks; however, the diffusion prior of progressively darkening facial content and the alternate gradient descent updates mitigate this by focusing on reflective regions. In low-light outdoor scenes, polarization observations may have reduced signal-to-noise ratio, but the method was validated on real-world eyeglass images captured under various indoor and outdoor lighting, achieving artifact-free reconstructions. No quantitative failure threshold is reported, but robustness is demonstrated across diverse lighting.
How does PDPrior achieve cost parity against legacy paired-dataset training methods, considering it requires no training data?
Legacy methods require large-scale paired polarization datasets, incurring costs for data collection, annotation, and retraining for each new lighting condition. PDPrior eliminates these costs entirely: it requires no training data and no ground-truth reflection-free images, operating solely on polarization observations collected at test time. The computational cost shifts to per-test optimization via diffusion sampling with alternate gradient descent updates, which is a one-time inference cost. This reduces deployment overhead and enables adaptation to unseen lighting without retraining, providing a favorable cost profile for video conferencing and facial recognition applications.
What are the scalability bottlenecks when deploying PDPrior on a division-of-focal-plane polarization camera for real-time video conferencing?
The primary bottleneck is the iterative diffusion sampling process, where reflection and transmission variables are alternately updated via gradient descent at each step. This per-frame optimization may challenge real-time throughput on standard hardware. However, the method operates on test-time polarization observations without training, so scaling to multiple users requires only parallel inference. The division-of-focal-plane polarization camera provides the necessary DoLP input. No specific frame rate or latency metrics are reported, but the framework's extension to driver monitoring and showcase displays suggests practical feasibility with adequate computational resources.
How does the physical forward model of reflection formation ensure interpretability compared to black-box deep learning approaches?
The forward model explicitly represents the captured image as a superposition of transmission and reflection layers. PDPrior uses DoLP to compute the transmission coefficient and guides transmission image generation via this physical model. The self-supervised loss based on the forward model alternately updates reflection and transmission variables through gradient descent, embedding physical constraints into the diffusion process. This contrasts with black-box networks that lack explicit layer separation, providing interpretability and enabling artifact-free, high-fidelity reconstructions validated on real-world eyeglass data.
What evidence supports the claim that PDPrior achieves higher face image quality assessment scores for recognition performance than state-of-the-art methods?
Extensive experimental results on eyeglass reflection data captured in both indoor and outdoor lighting conditions demonstrate that PDPrior produces high-fidelity face reconstructions with no visible artifacts. The method achieves higher face image quality assessment scores for recognition performance compared to state-of-the-art methods. These results validate effectiveness and robustness, directly addressing the occlusion problem that increases recognition errors in facial recognition-based biometric authentication. The specific metric values and statistical significance are not detailed in the provided text, but the comparative advantage is empirically established.
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