Machine learning assisted holography

Marshall Lindsay; Scott D. Kovaleski; Charlie Veal; Derek T. Anderson; Stanton R. Price

doi:10.1117/12.2585836

12 April 2021 Machine learning assisted holography

Marshall Lindsay, Scott D. Kovaleski, Charlie Veal, Derek T. Anderson, Stanton R. Price

Proceedings Volume 11731, Computational Imaging VI; 1173103 (2021) https://doi.org/10.1117/12.2585836
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Computer-generated holography (CGH) has enabled the formation of arbitrary images through complex spatial light modulation. The optimization of spatial light modulators (SLMs) and diffractive optical elements (DOEs) is aimed to solve the well-known phase retrieval problem. This paper proposes a physically constrained artificial neural network (ANN) designed to solve the phase retrieval problem for CGH. We show that through careful selection of model structural parameters and by limiting the scope of model optimization, we can encode Fresnel Diffraction equations directly into an ANN. We train the proposed model to overfit to a single image, i.e., the model finds the SLM phase delays required to produce the desired image. The proposed model performs well with outputs that qualitatively compare well with ideal images. The method proposed in this work holds value for those who require confidence that their machine learning techniques are physically realizable.

Conference Presentation

Citation Download Citation

Marshall Lindsay, Scott D. Kovaleski, Charlie Veal, Derek T. Anderson, and Stanton R. Price "Machine learning assisted holography", Proc. SPIE 11731, Computational Imaging VI, 1173103 (12 April 2021); https://doi.org/10.1117/12.2585836

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