Estimation-theoretic analysis of lensless imaging

TL;DR

This paper uses estimation theory to analyze lensless imaging systems, revealing how object sparsity influences system performance and tradeoffs in optical encoder design under different noise conditions.

Contribution

It introduces an estimation-theoretic framework to evaluate lensless imaging encoders, providing quantitative insights into the effects of object sparsity and noise models on system performance.

Findings

Sparse objects tolerate higher multiplexing levels.

Performance varies with object sparsity and noise type.

Design insights can improve lensless imaging systems.

Abstract

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.