Numerical estimation method for misalignment of optical systems using machine learning

Ryo Hashimoto; Shuji Matsuura; Yusuke Iida

doi:10.1117/12.2688930

28 November 2023 Numerical estimation method for misalignment of optical systems using machine learning

Ryo Hashimoto, Shuji Matsuura, Yusuke Iida

Proceedings Volume 12765, Optical Design and Testing XIII; 1276520 (2023) https://doi.org/10.1117/12.2688930
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Identifying the cause of aberration in general optical systems is difficult and time-consuming because it requires specific measurement and analysis. Therefore, we suggest a novel method based on deep learning to find out misalignment numerically from measured raw images at near-focus, that do not require specific measurements, without the time and effort of analysis. Taking advantage of deep learning, which numerically extracts features from images, our model takes a set of distorted images as input and outputs parameters indicating misalignment. We develop two deep learning models to predict the misalignment of optical systems, a parabolic mirror and a telescope, using a dataset generated through simulation. In spite of real measurement images have noise, the trained model for a parabolic mirror can predict misalignment parameter. Near-focus images suggested by the model exhibit the similar trend in PSF size and stretch direction to the measurement images. To elevate our methods to a practical level, we adjust the telescope in accordance with the model’s predictions. This adjustment results in improved symmetry of the images in the front-back focus direction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Ryo Hashimoto, Shuji Matsuura, and Yusuke Iida "Numerical estimation method for misalignment of optical systems using machine learning", Proc. SPIE 12765, Optical Design and Testing XIII, 1276520 (28 November 2023); https://doi.org/10.1117/12.2688930

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