Deep learning-based phase unwrapping for holographic tomography

Michał Gontarz; Vibekananda Dutta; Małgorzata Kujawińska

doi:10.1117/12.2673675

9 August 2023 Deep learning-based phase unwrapping for holographic tomography

Michał Gontarz, Vibekananda Dutta, Małgorzata Kujawińska

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210P (2023) https://doi.org/10.1117/12.2673675
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

The nature of phase retrieval algorithms in holographic tomography (HT) is the reason that, in order to achieve the refractive index (RI) reconstruction, phase unwrapping (PU) has to be carried out beforehand. Conventional PU algorithms present shortcomings regarding the speed, noise robustness, reliability, and automation possibilities. These problems can be tackled by implementation of deep learning (DL) and convolutional neural networks in the PU process. The work presents a two-step pipeline consisting of denoising and unwrapping models. Both steps are realised under the umbrella of a U-Net architecture; however, unwrapping, which is carried out via semantic segmentation, is aided by Attention Gates and Residual Blocks (RB) introduced to the model. Through the experiments, the proposed pipeline is capable of denoising and unwrapping highly irregular and complex phase images captured in HT. What is more, the pipeline is implemented into a tomographic reconstruction algorithm. The quality of the final RI distribution is evaluated against the reconstruction with the conventional State-of-the-Art solution. To our knowledge, this is the first work, that puts forward phase unwrapping via segmentation with an Attention U-Net with RBs, assisted by a pre-processing denoising step. Moreover, this is the first solution, based on DL, that is trained entirely on experimental phase images captured in HT, and whose performance is evaluated on the distribution of RI of the measured specimen.

Conference Presentation

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

Citation Download Citation

Michał Gontarz, Vibekananda Dutta, and Małgorzata Kujawińska "Deep learning-based phase unwrapping for holographic tomography", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210P (9 August 2023); https://doi.org/10.1117/12.2673675

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