引导式无镜头偏振成像
Guided Lensless Polarization Imaging
AI总结：
本文提出一种结合紧凑型偏振-RGB传感器和常规RGB相机的无镜头偏振成像系统，通过两阶段流程提升重建质量与通用性。
摘要：
偏振成像捕捉光的偏振状态，揭示人眼不可见但有价值的信息，广泛应用于生物医学诊断、自动驾驶和遥感等领域。然而，传统偏振相机往往昂贵且体积大，限制了实际应用。无镜头成像通过替换镜头为简单光学元件如扩散器，实现紧凑低成本的替代方案，但现有无镜头偏振系统存在重建质量有限的问题。为克服这些限制，我们引入一种RGB引导的无镜头偏振成像系统，结合紧凑型偏振-RGB传感器与辅助的常规RGB相机提供结构引导。通过两阶段流程重建每个RGB颜色通道的多角度偏振图像：物理建模的反演恢复初始偏振图像，随后通过基于Transformer的融合网络利用常规RGB相机的RGB引导图像优化重建。我们的两阶段方法在无镜头基准上显著提升了重建质量和保真度，跨数据集和成像条件泛化能力强，并在物理原型无镜头相机上实现了高质量的现实结果，无需任何微调。
英文摘要：
Polarization imaging captures the polarization state of light, revealing information invisible to the human eye yet valuable in domains such as biomedical diagnostics, autonomous driving, and remote sensing. However, conventional polarization cameras are often expensive, bulky, or both, limiting their practical use. Lensless imaging offers a compact, low-cost alternative by replacing the lens with a simple optical element like a diffuser and performing computational reconstruction, but existing lensless polarization systems suffer from limited reconstruction quality. To overcome these limitations, we introduce a RGB-guided lensless polarization imaging system that combines a compact polarization-RGB sensor with an auxiliary, widely available conventional RGB camera providing structural guidance. We reconstruct multi-angle polarization images for each RGB color channel through a two-stage pipeline: a physics-based inversion recovers an initial polarization image, followed by a Transformer-based fusion network that refines this reconstruction using the RGB guidance image from the conventional RGB camera. Our two-stage method significantly improves reconstruction quality and fidelity over lensless-only baselines, generalizes across datasets and imaging conditions, and achieves high-quality real-world results on our physical prototype lensless camera without any fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
