ArticleBiomedical optics express2021
Automated phase unwrapping in digital holography with deep learning.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.Biomedical optics express · 2026Review
- Real-time processing of high-throughput quantitative phase microscopy data using a Jetson Orin Nano.Biophotonics discovery · 2026Article
- Calibration of Digital Holographic Camera for Bubble Gas Volumetric Flux Measurements.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Optical Metrology and Perception Technologies.Sensors (Basel, Switzerland) · 2025Review
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Beyond conventional microscopy: Observing kidney tissues by means of fourier ptychography.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between -π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.