Deep convolutional neural network-based lensless quantitative phase retrieval

Igor Shevkunov; Jarkko Kilpeläinen; Karen Eguiazarian

doi:10.1117/12.2581428

5 March 2021 Deep convolutional neural network-based lensless quantitative phase retrieval

Igor Shevkunov, Jarkko Kilpeläinen, Karen Eguiazarian

Proceedings Volume 11653, Quantitative Phase Imaging VII; 116531F (2021) https://doi.org/10.1117/12.2581428
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval problem in a lensless optical system from a single observation. We utilize U-net structured DCNN to reconstruct phase from the amplitude images at the sensor plane, and after applying computational backpropagation, complex objects' amplitude is reconstructed at the object plane. Results are demonstrated by simulation experiments.

Conference Presentation

Citation Download Citation

Igor Shevkunov, Jarkko Kilpeläinen, and Karen Eguiazarian "Deep convolutional neural network-based lensless quantitative phase retrieval", Proc. SPIE 11653, Quantitative Phase Imaging VII, 116531F (5 March 2021); https://doi.org/10.1117/12.2581428

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