ReviewLight, science & applications2024
On the use of deep learning for phase recovery.
Review in Light, science & applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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The trial behind it
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Who cites it
31 citing papers in PubMed.
- Polarization-Resolved Digital Holographic Sensing for Simultaneous Morphological and Polarimetric Characterization of Microparticles.Sensors (Basel, Switzerland) · 2026Article
- Quadri-Wave Lateral Shearing Interferogram Outpainting Using QW-GAN for Accurate Wavefront Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- AI-driven dual-mode phase and label-free fluorescence imaging platform using a single-shot gabor hologram.Microsystems & nanoengineering · 2026Article
- DSHARP: Deep Incompressible Motion Estimation With Sinusoidal-Transformed Harmonic Phase for Tagged MRI.IEEE transactions on medical imaging · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- An end-to-end hybrid deep-learning approach for single-shot wavefront sensing and correction.Nature communications · 2026Article
- AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.Biomedical optics express · 2026Review
- Phase and absorbance retrieval in X-ray holographic microscopy under weak illumination using physics-driven neural networks.Journal of synchrotron radiation · 2026Article
- Upconversion optical entropy encoding for infrared complex-amplitude imaging.Light, science & applications · 2026Article
- A Comprehensive Review of Optical Metrology and Perception Technologies.Sensors (Basel, Switzerland) · 2025Review
- Ultrafast Dynamic Defect Inspection With Computational Neuromorphic Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Fourier ptychographic enhancement of iterative pathways: autonomous 3D momentum coordination in hybrid ML-PIE architectures.Biomedical optics express · 2025Article
- Descattering and image restoration with a transformer-based neural network in deep tissue imaging.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Lightweight and precise cell classification based on holographic tomography-derived refractive index point cloud.Journal of biomedical optics · 2025Article
- Development of a deep learning method for phase retrieval image enhancement in phase contrast microcomputed tomography.Journal of microscopy · 2025Article
- Deep learning ocular aberration retrieval from simulated retinal images under straylight.Biomedical optics express · 2025Article
- Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network.Nature communications · 2025Article
- In situ fully vectorial tomography and pupil function retrieval of tightly focused fields.Nature communications · 2025Article
- Tailoring spatiotemporal wavepackets via two-dimensional space-time duality.Nature communications · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.
Identifiers
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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.