Related papers: Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems

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

URL: http://arxiv.org/abs/2506.23173v1
Date: Sun, 29 Jun 2025 10:13:40 GMT
Title: Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems
Authors: Tomer Slor, Dean Oren, Shira Baneth, Tom Coen, Haim Suchowski,
Abstract summary: We present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems.<n>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.<n>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.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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