Impact of optical aberrations on image classification with deep neural networks

Page King; R. John Koshel

doi:10.1117/12.2631663

3 October 2022 Impact of optical aberrations on image classification with deep neural networks

Page King, R. John Koshel

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270E (2022) https://doi.org/10.1117/12.2631663
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Robustness to image quality degradations is critical for developing deep neural networks for real-world image classification. Previous efforts have pursued robustness by exploring how various types of blur, noise, contrast, compression, color, etc. degrade image quality and impact image classification performance. This paper extends this discussion to include optical aberrations, which are fundamental to the lens design of imaging systems and enable further discussion of DNN performance in the context of hardware design. In this paper, multiple state-of-the-art DNN models are evaluated for their image classification performance with imagery that has been degraded by various optical aberrations.

Conference Presentation

Citation Download Citation

Page King and R. John Koshel "Impact of optical aberrations on image classification with deep neural networks", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270E (3 October 2022); https://doi.org/10.1117/12.2631663

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