用信息理论分析无透镜成像，揭示物体稀疏性对编码器设计的影响。
Estimation-theoretic analysis of lensless imaging
- 基于费舍尔信息分析多种光学编码器性能
- 稀疏物体可承受更高程度的编码复用
- 适合光学系统设计者参考优化编码结构
我们采用估计理论中的费舍尔信息方法，分析无透镜成像系统的性能。在高斯和泊松噪声模型下，评估了多种光学编码器设计在不同稀疏度物体上的表现。仿真结果表明，无透镜成像性能依赖于物体特性，编码复用与物体稀疏性之间存在权衡：稀疏物体可容忍更高程度的复用。该分析为优化无透镜成像的光学编码器设计提供了定量指导。
原文摘要 · Abstract (English)
We analyze lensless imaging systems with estimation-theoretic techniques based on Fisher information. Our analysis evaluates multiple optical encoder designs on objects with varying sparsity, in the context of both Gaussian and Poisson noise models. Our simulations verify that lensless imaging system performance is object-dependent and highlight tradeoffs between encoder multiplexing and object sparsity, showing quantitatively that sparse objects tolerate higher levels of multiplexing than dense objects. Insights from our analysis promise to inform and improve optical encoder designs for lensless imaging.
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