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Updated: Jan 28, 2026

Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Phase recovery and holographic image reconstruction using deep learning in neural networks
Yair Rivenson1,2,3, Yibo Zhang1,2,3, Harun Günaydın1
1Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Abstract:
Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. In this study, we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts. This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram, requiring fewer measurements in addition to being computationally faster. We validated this method by reconstructing the phase and amplitude images of various samples, including blood and Pap smears and tissue sections. These results highlight that challenging problems in imaging science can be overcome through machine learning, providing new avenues to design powerful computational imaging systems.
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