# Quaternion Tensor Modeling for Joint Color-Polarization Demosaicking

arXiv: https://arxiv.org/abs/2608.02144 · HTML: https://mlsanity.com/paper/2608.02144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation](https://mlsanity.com/paper/2007.14292/) (2020)
- [PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution](https://mlsanity.com/paper/2504.07758/) (2025)
- [Two-Step Color-Polarization Demosaicking Network](https://mlsanity.com/paper/2209.06027/) (2022)
- [Polarization Uncertainty-Guided Diffusion Model for Color Polarization Image Demosaicking](https://mlsanity.com/paper/2602.23847/) (2026)
- [Quaternion Tensor Completion with Sparseness for Color Video Recovery](https://mlsanity.com/paper/2212.08361/) (2022)
- [Efficient Polarization Demosaicking via Low-cost Edge-aware and Inter-channel Correlation](https://mlsanity.com/paper/2408.17099/) (2024)

## Abstract (arXiv)

Division-of-focal-plane (DoFP) color polarization cameras enable snapshot acquisition of color polarization mosaic images, but the inherently sparse sampling pattern makes color polarization demosaicking severely ill-posed. Existing methods often fail to jointly exploit the correlations among polarization channels and the physical constraints inherent in polarization imaging, resulting in noticeable demosaicking artifacts. To address this issue, a quaternion-tensor-based color polarization demosaicking (CPDM) method incorporating Stokes-domain total variation (TV) regularization is proposed. Correlation analysis shows that the correlations among polarization channels are stronger than those among color channels. Accordingly, the color polarization images acquired at 0^circ, 45^circ, 90^circ, and 135^circ are encoded into the four components of a third-order quaternion tensor, with the color channels organized along its third mode. A low-rank prior is then imposed on the quaternion tensor to exploit the global structural redundancy in the color polarization data. Moreover, spatial gradients are mapped to the Stokes domain through an orthogonal transformation to separate intensity, polarization and residual variations, with adaptive quaternion weights enabling component-specific regularization and preserving the energy consistency of the reconstructed Stokes vectors. An efficient optimization algorithm is derived for the resulting model. Extensive experiments demonstrate the superior demosaicking performance of the proposed method.