A Hybrid Polarization Image Demosaicking Algorithm Based on Inter-Channel Correlation

Abstract

Emerging monochrome and chromatic polarization filter array (MPFA and CPFA) cameras require polarization demosaicking to obtain accurate polarization parameters. Polarization cameras sample the polarization intensity at each location of the pixels. A captured raw image must be converted to a full-channel polarization intensity image using the polarization demosaicking method (PDM). However, due to sparse sampling between polarization channels, implementing MPFA and CPFA demosaicking has been challenging. This paper proposes a new hybrid polarization demosaicking algorithm that leverages polarization confidence-based refinement to exploit inter-channel polarization correlation. Additionally, we enhance texture correlation to utilize inter-channel texture correlation fully. Our three-stage PDM preserves both the polarization and texture information. We also introduce a metric computation method to handle the $\pi$ -ambiguity of the angle of line polarization (AoLP). This approach mitigates inaccuracies and $\pi$ -ambiguity in existing methods when describing the quality of AoLP reconstruction. We extensively compare and conduct ablation experiments on synthetic datasets from MPFA and CPFA. Our method achieves competitive results compared to other state-of-the-art methods. Furthermore, we evaluate our proposal on real-world datasets to demonstrate its applicability in real-world, variable scenarios. Two application experiments (road detection and shape from polarization) show that our proposal can be applied to real-world applications.