用一块石英片实现微型化高维光场光谱成像,突破分辨率瓶颈。
Aperture-aware Dispersion 5-D Light-field Imaging Spectrometer

- 通过双折射材料的孔径编码,将多视角信息融合到单个传感器像素上
- 在真实实验中实现全空间分辨率的5D光场光谱数据重建
- 适合需要紧凑高维感知的智能设备、可穿戴系统等场景
在提升感知维度的同时实现成像系统小型化,对高维视觉传感构成重大挑战。传统5D(x,y,u,v,λ)光谱光场(5D-SLF)数据立方体获取依赖庞大且昂贵的相机阵列,难以广泛应用。现有单探测器系统因编码能力不足,各维度分辨率存在根本性权衡。本文提出孔径感知色散光场光谱仪(ADLIS),通过孔径多路复用调制实现紧凑性与分辨率的协同优化,利用双折射材料的固有光谱滤波特性。仅需一块制造友好且低成本的双折射石英晶体制成的相位板,即可实现对入射光角度和光谱高度敏感的紧凑角-谱编码。与微透镜阵列的视点分离方法不同,ADLIS采用孔径编码,将所有视点叠加至单一传感器像素。这一设计范式从空间分割转向编码集成,旨在实现全分辨率光场恢复。因此,我们构建了端到端(E2E)的孔径感知色散光场成像(ADLI)框架,联合优化孔径设计与5D-SLF重构。基于仿真数据训练并在真实世界实验中验证,系统实现了鲁棒的高性能5D-SLF成像,同时保持全空间分辨率。
原文摘要 · Abstract (English)
Enhancing perceptual dimensions while miniaturizing imaging systems presents significant challenges for high-dimensional visual sensing. Conventionally, the acquisition of the 5D (x,y,u,v,λ) spectral light field (5D-SLF) data cube relies on bulky and expensive camera arrays, which are impractical for widespread application. Existing single-detector systems are fundamentally limited by a trade-off between the resolutions of different dimensions owing to insufficient coding capabilities. Here we introduce an Aperture-aware Dispersion Light-field Imaging Spectrometer (ADLIS), that targets a synergy between compactness and resolution through aperture-multiplexed modulation, leveraging the inherent spectral-filtering properties of birefringent material. Using only a manufacturing-friendly and cost-effective phase plate made of birefringent quartz crystal, the aperture of the proposed ADLIS enables compact angular-spectral encoding that is highly sensitive to both the incident angle and spectrum of incoming light. In contrast to the viewpoint-separation approach of microlens arrays, ADLIS employs aperture encoding to superimpose all viewpoints onto each sensor pixel. This shifts the design paradigm from spatial division to encoding integration, aiming to achieve full-resolution light field recovery. Thus, we develop the Aperture-aware Dispersion Light-field Imaging (ADLI) framework, which optimizes the aperture design and 5D-SLF reconstruction in an end-to-end (E2E) manner. Trained by simulation data and validated through real-world experiments, our system achieves robust high-performance 5D-SLF imaging while maintaining full spatial resolution.
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