DoFP-Aligned Lookup Tables for Real-Time Polarization Demosaicking

Real-time polarization imaging with off-the-shelf color division-of-focal-plane (DoFP) cameras requires demosaicking that is both accurate and deployment-friendly. Interpolation methods are lightweight but limited in polarization fidelity, whereas learning-based methods improve quality at much higher online cost. We propose DoFP-LUT, a sensor-structured LUT framework and, to our knowledge, the first LUT-based framework for real-time color DoFP polarization demosaicking. It leverages the deterministic and memory-light nature of LUT inference while aligning lookup corrections with DoFP-specific residual structures. Unlike generic LUTs designed for RGB restoration, DoFP-LUT targets post-initialization residuals that are coupled across analyzer orientations, Stokes relations, and local spatial structures. It factorizes teacher-guided correction into shared analyzer correction, polarization redistribution, and high-frequency compensation, compiling each component into compact low-dimensional LUTs offline. Online inference then requires only nearest-neighbor lookup and fixed write-back. Experiments show that DoFP-LUT achieves a favorable quality--efficiency trade-off, improving polarization reconstruction while preserving deterministic, memory-light inference for real-time polarization imaging.