Pixel super-resolution and autofocusing in holographic imaging through deep learning

Hanlong Chen; Luzhe Huang; Tairan Liu; Aydogan Ozcan

doi:10.1117/12.3027384

4 October 2024 Pixel super-resolution and autofocusing in holographic imaging through deep learning

Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311817 (2024) https://doi.org/10.1117/12.3027384
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

We introduce the enhanced Fourier Imager Network (eFIN), an end-to-end deep neural network that synergistically integrates physics-based propagation models with data-driven learning for highly generalizable hologram reconstruction. eFIN overcomes a key limitation of existing methods by performing seamless autofocusing across a large axial range without requiring a priori knowledge of sample-to-sensor distances. Moreover, eFIN incorporates a physics-informed sub-network that accurately infers unknown axial distances through an innovative loss function. eFIN can also achieve a three-fold pixel super-resolution, increasing the space-bandwidth product by nine-fold and enabling substantial acceleration of image acquisition and processing workflows with a negligible performance penalty.

Conference Presentation

Citation Download Citation

Hanlong Chen, Luzhe Huang, Tairan Liu, and Aydogan Ozcan "Pixel super-resolution and autofocusing in holographic imaging through deep learning", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311817 (4 October 2024); https://doi.org/10.1117/12.3027384

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