ZeroPIE: Zero-Reference Polarization-Aware Low-Light Enhancement via Physically Derived Illumination-Invariant Priors

Polarization imaging extends the capabilities of traditional vision systems by capturing rich multidimensional data essential for various tasks. However, color polarization cameras suffer from severe performance degradation under low-light conditions, which limits their use in real-world environments. Existing polarization-aware low-light enhancement (PLLE) methods mainly rely on supervised learning, which makes them sensitive to training data distributions and light-specific hyperparameters, with poor cross-domain generalization. To address these challenges, we propose ZeroPIE, the first zero-reference framework for PLLE trained exclusively on normal-light polarization images. Deviating from conventional data-driven paradigms, we derive four physically grounded illumination-invariant priors: Degree of Linear Polarization (DoLP), Angle of Linear Polarization (AoLP), geometric invariant, and chromatic invariant. These priors serve as robust, lighting-independent conditions that bridge low-light and normal-light polarization images. We further propose a recovery framework based on the Residual Denoising Diffusion Model (RDDM), where the proposed priors guide the reconstruction of normal-light polarization images without using real low-light data during training. Extensive experiments on both synthetic and real-world datasets demonstrate that ZeroPIE outperforms state-of-the-art methods in radiance restoration, polarization parameter reconstruction and downstream vision tasks, exhibiting strong robustness to unknown real-world degradations.