Adaptive 3D descattering with a dynamic synthesis network

Waleed Tahir; Lei Tian; Hao Wang

doi:10.1117/12.2594062

1 August 2021 Adaptive 3D descattering with a dynamic synthesis network

Waleed Tahir, Lei Tian, Hao Wang

Author Affiliations +

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180418 (2021) https://doi.org/10.1117/12.2594062
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

3D particle-localization using in-line holography is a fundamental problem with important applications. It involves estimating the unknown positions of scatterers in a 3D volume from a single 2D hologram. We propose a deep learning based framework that is highly computationally efficient for large-scale 3D reconstructio and demonstrates accurate results for a wide variety of scattering scenarios. The proposed approach incorporates physical scattering information into the result via 3D backpropagation of the hologram, followed by artifact removal with an end-to-end 3D deep neural network (DNN). To address the challenge of limited data availability, we train our DNN solely on simulated data, and show that it works accurately for experimental data as well. The results show that our DNN is able to accurately localize particles under various scattering scenarios with little computational overhead.

Conference Presentation

Citation Download Citation

Waleed Tahir, Lei Tian, and Hao Wang "Adaptive 3D descattering with a dynamic synthesis network", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180418 (1 August 2021); https://doi.org/10.1117/12.2594062

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