用扩散模型提升偏振图像去马赛克精度，尤其改善高误差区域的恢复效果。
Polarization Uncertainty-Guided Diffusion Model for Color Polarization Image Demosaicking
- 引入文本到图像扩散先验，增强模型对偏振特征的建模能力。
- 通过显式建模偏振不确定性，引导扩散过程修复高误差区域。
- 在真实数据集上实现高保真度的偏振信息重建，适合偏振成像应用。
彩色偏振去马赛克（CPDM）旨在从彩色偏振滤波阵列（CPFA）原始图像中重建四方向全分辨率偏振图像。由于需预测大量缺失像素且高质量训练数据稀缺，现有基于网络的方法虽能有效恢复场景亮度信息，但在偏振特性（偏振度DOP和偏振角AOP）重建上仍存在显著误差。为此，本文引入文本到图像（T2I）模型中的图像扩散先验，以克服网络方法的性能瓶颈，并通过额外的扩散先验弥补因数据分布受限导致的表征能力不足。为有效利用扩散先验，我们在重建过程中显式建模偏振不确定性，并利用该不确定性引导扩散模型修复高误差区域。大量实验表明，所提方法能以高保真度准确恢复场景偏振特性，兼具优异视觉感知效果。
原文摘要 · Abstract (English)
Color polarization demosaicking (CPDM) aims to reconstruct full-resolution polarization images of four directions from the color-polarization filter array (CPFA) raw image. Due to the challenge of predicting numerous missing pixels and the scarcity of high-quality training data, existing network-based methods, despite effectively recovering scene intensity information, still exhibit significant errors in reconstructing polarization characteristics (degree of polarization, DOP, and angle of polarization, AOP). To address this problem, we introduce the image diffusion prior from text-to-image (T2I) models to overcome the performance bottleneck of network-based methods, with the additional diffusion prior compensating for limited representational capacity caused by restricted data distribution. To effectively leverage the diffusion prior, we explicitly model the polarization uncertainty during reconstruction and use uncertainty to guide the diffusion model in recovering high error regions. Extensive experiments demonstrate that the proposed method accurately recovers scene polarization characteristics with both high fidelity and strong visual perception.
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