AI 中文总结
本文提出任务相关的费希尔信息框架，对比FLFM、SLIM等光场显微镜的性能，发现SLIM在探测器带宽受限下的费希尔信息等指标更优，为光学编码与相机读出协同设计提供通用基础。
AI 中文摘要
光场显微镜能够实现快照体积成像，但其信息速率受光学编码和探测器读出架构的共同限制。本文开发了一种任务相关的费希尔信息框架，用于评估相对于探测器资源（限制采集吞吐量）的光学编码器性能。在相同的光学几何结构、光子预算和行受限相机模型下，我们对比了全傅里叶光场显微镜（FLFM）、压缩光场显微镜（SLIM）以及帧率匹配的FLFM。针对稀疏场景，采用三维点发射器费希尔矩阵进行分析；针对密集场景，则利用倾斜光谱切片上的傅里叶模式信息开展分析。当s=0.25时，SLIM在每相机带宽下提供的轴向费希尔信息比帧率匹配的FLFM高2.40倍，三维D最优位置信息高1.89倍；对于密集场景，其每带宽下的积分傅里叶模式费希尔信息高1.85倍，轴向频率范围大4倍，投影横向硬支撑面积约大11倍。对压缩因子和视角倾斜的扫描分析表明，这些优势反映了通用的探测器分配原理，而非特定工作点。更广泛而言，该框架可通过纳入特定架构的测量模型和探测器吞吐量成本，适配其他相机架构，为光学编码、场景统计与相机读出的协同设计提供通用基础。
英文摘要
Light-field microscopy enables snapshot volumetric imaging, but its information rate is constrained by both optical encoding and detector readout architecture. Here we develop a task-dependent Fisher-information framework that evaluates optical encoders relative to the detector resource limiting acquisition throughput. We compare full Fourier light-field microscopy (FLFM), squeezed light-field microscopy (SLIM), and frame-rate-matched FLFM under a common optical geometry, photon budget, and row-limited camera model. Sparse scenes are analyzed using a 3D point-emitter Fisher matrix, and dense scenes using Fourier-mode information on tilted spectral slices. At s=0.25, SLIM provides 2.40x higher axial Fisher information per camera bandwidth and 1.89x higher 3D D-optimal position information than frame-rate-matched FLFM. For dense scenes, it provides 1.85x higher integrated Fourier-mode Fisher information per bandwidth, 4x greater axial-frequency extent, and approximately 11x larger projected lateral hard-support area. Sweeps over compression factor and view tilt show that these advantages reflect a general detector-allocation principle rather than a specific operating point. More broadly, the framework can be adapted to other camera architectures by incorporating architecture-specific measurement models and detector-throughput costs, providing a general basis for co-designing optical encoding, scene statistics, and camera readout.
