Division-of-focal-plane polarization image demosaicking using multi-level texture-aware inter-channel correlation

Polarization imaging using division-of-focal-plane (DoFP) sensors enables simultaneous capture of polarization information, but their super-pixel structure introduces aliasing artifacts after demosaicking. This paper presents a three-stage polarization image demosaicking (PIDM) method using inter-channel interpolation to guide the reconstruction of the missing components. In addition, multi-scale texture-aware guided filtering with confidence-aware fusion is employed to refine both textured and smooth regions. Finally, an objective function combining confidence levels with correlations among the demosaicked image, DoFP input, and Stokes parameters is minimized using Adam's optimization. The method is implemented in two variants with different complexities. Experiments with real DoFP sensor data show that they surpass existing methods in the root mean square error (RMSE) and structural similarity index measure (SSIM) by at least 33.02% and 7.85%, respectively. The results on simulated skylight polarization images further validate its accuracy. The method is highly parallelizable, achieving a 16 × speedup on an Nvidia GTX 1060 GPU over an Intel i5-8300H CPU for a 512 × 612 × 4 input.