基于超表面的快照高光谱成像中的空间-光谱权衡
Spatial-Spectral Trade-offs in Metasurface-Based Snapshot Hyperspectral Imaging
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中文总结 AI 辅助
该研究通过可微超表面模型分析空间-光谱权衡，发现柱库波长透射受限导致光谱坍缩，并提出了局部超像素光谱仪模型以分离研究光谱编码。
中文摘要 AI 辅助
快照高光谱成像将空间-光谱数据立方体映射到二维探测器上，其可恢复性取决于数据立方体的采样方式、场景先验以及光学编码。我们通过改变空间-光谱比，并在可微分的超表面模型中比较平均相干性、互信息和端到端目标，来研究这种共同依赖性，同时结合算法恢复与光学设计优化。对于三波段的压缩成像，对单参数介质柱的优化会在所测试的目标中产生类似透镜的设计，这些设计具有重叠的波长相关点扩散函数。端到端重建可以在受限的训练分布上掩盖这种坍缩，这表明重建保真度共同取决于光学编码和场景先验。对于简单的纳米柱超原子几何结构，随着数据立方体在光谱上占主导地位，优化后的奇异值谱会恶化，而一个在每个波长处具有独立复透射的松弛模型则减少了这种依赖性。这些结果指出，柱库中受限的波长相关透射控制是观察到的光谱坍缩的一个重要因素。这促使我们构建一个示例性的局部超像素光谱仪模型，在该模型中，光谱编码在平面波照明近似下与全局图像形成分开研究。
英文摘要
Snapshot hyperspectral imaging maps a spatial--spectral datacube onto a two-dimensional detector, with recoverability depending on the datacube sampling regime, the scene prior, and the optical encoding. We study this co-dependence with algorithmic recovery and optical design optimization in a differentiable metasurface model by varying the spatial--spectral ratio and comparing average-coherence, mutual-information, and end-to-end objectives. For compressed imaging with three bands, optimization of single-parameter dielectric pillars produces lens-like designs with overlapping wavelength-dependent point-spread functions across the objectives tested. End-to-end reconstruction can mask this collapse on restricted training distributions, showing that reconstruction fidelity depends jointly on the optical encoding and scene prior. For simple nano-pillar meta-atom geometries, the optimized singular-value spectrum deteriorates as the datacube becomes more spectrally dominated, whereas a relaxed model with independent complex transmission at each wavelength reduces this dependence. These results identify restricted wavelength-dependent transmission control in the pillar library as an important contributor to the observed spectral collapse. This motivates an illustrative local super-pixel spectrometer model in which spectral encoding is studied separately from global image formation under a plane-wave illumination approximation.
发表机构
- McGill University（麦吉尔大学）
- Polytechnique Montréal（蒙特利尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。
