发表机构
Yunnan University; University of Macau; Southwestern University of Finance and Economics（云南大学; 澳门大学; 西南财经大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对焦平面分割颜色偏振相机的去马赛克伪影问题，提出结合斯托克斯域全变分正则化的四元数张量建模方法，通过挖掘偏振通道相关性与全局冗余，实现了更优的去马赛克性能。
AI 中文摘要
焦平面分割（DoFP）颜色偏振相机可快照式采集颜色偏振马赛克图像，但固有的稀疏采样模式使颜色偏振去马赛克问题严重不适定。现有方法常无法联合利用偏振通道间的相关性及偏振成像固有的物理约束，导致明显的去马赛克伪影。为解决该问题，本文提出一种结合斯托克斯域全变分（TV）正则化的基于四元数张量的颜色偏振去马赛克（CPDM）方法。相关性分析表明，偏振通道间的相关性强于颜色通道间的相关性。据此，将采集到的0°、45°、90°、135°的颜色偏振图像编码为三阶四元数张量的四个分量，颜色通道沿其三阶模式排列。随后对四元数张量施加低秩先验，以挖掘颜色偏振数据中的全局结构冗余。此外，通过正交变换将空间梯度映射至斯托克斯域，以分离强度、偏振及残差变化，自适应四元数权重支持分量特定正则化并保留重建斯托克斯向量的能量一致性。为所得模型推导了高效优化算法，大量实验证明了所提方法优越的去马赛克性能。
英文摘要
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.
