Quaternion Tensor Modeling for Joint Color-Polarization Demosaicking
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the problem of recovering full color and polarization information from images taken by cameras that capture only sparse color polarization samples at once. They propose a new method that packs the data into a special math object called a quaternion tensor, which helps capture relationships between different polarization angles and colors better. They also use a technique that smooths the image based on physical properties of how light polarization works. Their approach improves the quality of the reconstructed images compared to previous methods. Experiments showed that their method reduces errors in filling in missing data.
Division-of-focal-plane (DoFP) cameraColor polarization imagingDemosaickingQuaternion tensorStokes parametersTotal variation regularizationLow-rank priorPolarization channelsOrthogonal transformationOptimization algorithm
Authors
Yanqing Song, Jifei Miao, Chaoqian Li, Rui Mei, Kit Ian Kou, Liqiao Yang
Abstract
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.