Chromatic aberration correction using reinforcement learning

Katharina Schmidt; Ning Guo; Wenjie Wang; Jürgen Czarske; Nektarios Koukourakis

doi:10.1117/12.2675887

28 September 2023 Chromatic aberration correction using reinforcement learning

Katharina Schmidt, Ning Guo, Wenjie Wang, Jürgen Czarske, Nektarios Koukourakis

Proceedings Volume 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; 126550A (2023) https://doi.org/10.1117/12.2675887
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

Chromatic aberrations can significantly diminish image quality when employing multiple wavelengths for imaging with a single optical system due to dispersion in both optical system and samples. To tackle this problem, we propose a novel approach using an adaptive achromatic lens, which is controlled by a trained Reinforcement Learning agent as part of a machine learning method. Notably, our method corrects chromatic aberrations prior to the imaging process, distinguishing it from conventional software-based post-processing approaches.

Conference Presentation

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

Citation Download Citation

Katharina Schmidt, Ning Guo, Wenjie Wang, Jürgen Czarske, and Nektarios Koukourakis "Chromatic aberration correction using reinforcement learning", Proc. SPIE 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, 126550A (28 September 2023); https://doi.org/10.1117/12.2675887

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