Pupil Design for Computational Wavefront Estimation

TL;DR

This paper introduces a quantitative asymmetry metric for pupil design, demonstrating that increased asymmetry improves wavefront estimation accuracy through extensive simulations and experiments.

Contribution

It provides a new metric for pupil asymmetry and empirical evidence showing that greater asymmetry enhances wavefront recoverability.

Findings

Increasing pupil asymmetry improves wavefront estimation accuracy.

Enhanced asymmetry leads to better recoverability in noisy conditions.

Trade-offs include light throughput reduction with higher asymmetry.

Abstract

Establishing a precise connection between imaged intensity and the incident wavefront is essential for emerging applications in adaptive optics, holography, computational microscopy, and non-line-of-sight imaging. While prior work has shown that breaking symmetries in pupil design enables wavefront recovery from a single intensity measurement, there is little guidance on how to design a pupil that improves wavefront estimation. In this work we introduce a quantitative asymmetry metric to bridge this gap and, through an extensive empirical study and supporting analysis, demonstrate that increasing asymmetry enhances wavefront recoverability. We analyze the trade-offs in pupil design, and the impact on light throughput along with performance in noise. Both large-scale simulations and optical bench experiments are carried out to support our findings.