Pupil Design for Computational Wavefront Estimation

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.

Figures (21)

• Figure 1: Our work investigates the role of pupil design in wavefront estimation, proposes an asymmetry metric to gauge pupil performance, and provides thorough empirical evidence to support our findings. It directly impacts fields like adaptive optics and microscopy, where accurate wavefront estimation is crucial.
• Figure 2: Visualization of trivial ambiguities. Different phase aberrations (or any combinations of them) yield the same point spread function (PSF), resulting in a many-to-one relationship for the wavefront estimation problem.
• Figure 3: The asymmetry metric $\alpha$ is defined as the maximum non-overlapping area between the pupil and its flip about its center. A pupil can therefore be decomposed into two parts: a symmetric part that is invariant over flipping, and an asymmetric part. The symmetric part of the pupil contributes to the ambiguities in the intensity measurement, while the asymmetric parts encode distinguishable intensities.
• Figure 4: Overview of the pipeline of this paper. A dataset of pupils is generated to uniformly cover a range of asymmetry values. A large dataset of phase-pupil-PSF triplets is used to train a network in a supervised fashion.
• Figure 5: Qualitative light throughput and noise results. With our simulation, more asymmetric pupils yield lower PSNR even when the noise variance ($\sigma^{2}$) is fixed, reflecting a physics-grounded light throughput simulation. The PSNR of each PSF is noted, with the average PSNR for that specific pupil and noise sigma in parentheses.

Theorems & Definitions (2)

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