Polarization-guided diffusion prior for eyeglass reflection removal

• • 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.