# Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems

arXiv: https://arxiv.org/abs/2506.23173 · HTML: https://mlsanity.com/paper/2506.23173/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data](https://mlsanity.com/paper/2011.10243/) (2020)
- [Tolerance-Aware Deep Optics](https://mlsanity.com/paper/2502.04719/) (2025)
- [Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks](https://mlsanity.com/paper/2311.18214/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Real-world Lens Active Alignment with Unlabeled Data via Domain Adaptation](https://mlsanity.com/paper/2601.03718/) (2026)

## Abstract (arXiv)

In the rapidly evolving field of optical engineering, precise alignment of multi-lens imaging systems is critical yet challenging, as even minor misalignments can significantly degrade performance. Traditional alignment methods rely on specialized equipment and are time-consuming processes, highlighting the need for automated and scalable solutions. We present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems using only optical measurements. First, we use ray-traced spot diagrams to predict five-degree-of-freedom (5-DOF) errors in a 6-lens photographic prime, achieving a mean absolute error of 0.031mm in lateral translation and 0.011^circ in tilt. We also introduce a physics-based simulation pipeline that utilizes grayscale synthetic camera images, enabling a deep learning model to estimate 4-DOF, decenter and tilt errors in both two- and six-lens multi-lens systems. These results show the potential to reshape manufacturing and quality control in precision imaging.