# Pupil Design for Computational Wavefront Estimation

arXiv: https://arxiv.org/abs/2604.00225 · HTML: https://mlsanity.com/paper/2604.00225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Phase Retrieval via Wirtinger Flow: Theory and Algorithms](https://mlsanity.com/paper/1407.1065/) (2014)
- [prDeep: Robust Phase Retrieval with a Flexible Deep Network](https://mlsanity.com/paper/1803.00212/) (2018)
- [Phase Retrieval from Coded Diffraction Patterns](https://mlsanity.com/paper/1310.3240/) (2013)
- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [Solving Phase Retrieval with a Learned Reference](https://mlsanity.com/paper/2007.14621/) (2020)
- [Encoding complex fields by using a phase-only optical element: mitigation of pixel crosstalk effects](https://mlsanity.com/paper/1903.06046/) (2019)
- [WaveMo: Learning Wavefront Modulations to See Through Scattering](https://mlsanity.com/paper/2404.07985/) (2024)
- [Wavefront Estimation From a Single Measurement: Uniqueness and Algorithms](https://mlsanity.com/paper/2504.09395/) (2025)
- [V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy](https://mlsanity.com/paper/2504.07853/) (2025)
- [Guidestar-Free Adaptive Optics with Asymmetric Apertures](https://mlsanity.com/paper/2602.07029/) (2026)

## Abstract (arXiv)

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.