ArticleLight, science & applications2020
Learning to synthesize: robust phase retrieval at low photon counts.
Article in Light, science & applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Phase and absorbance retrieval in X-ray holographic microscopy under weak illumination using physics-driven neural networks.Journal of synchrotron radiation · 2026Article
- Hybrid Iterating-Averaging Low Photon Budget Gabor Holographic Microscopy.ACS photonics · 2025Article
- Self-supervised dynamic learning for long-term high-fidelity image transmission through unstabilized diffusive media.Nature communications · 2024Article
- Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.Optics express · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- A sub-wavelength Si LED integrated in a CMOS platform.Nature communications · 2023Article
- Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization.Light, science & applications · 2022Article
- Computer-free computational imaging: optical computing for seeing through random media.Light, science & applications · 2022Article
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5 authors.
Funding
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Abstract
The quality of inverse problem solutions obtained through deep learning is limited by the nature of the priors learned from examples presented during the training phase. Particularly in the case of quantitative phase retrieval, spatial frequencies that are underrepresented in the training database, most often at the high band, tend to be suppressed in the reconstruction. Ad hoc solutions have been proposed, such as pre-amplifying the high spatial frequencies in the examples; however, while that strategy improves the resolution, it also leads to high-frequency artefacts, as well as low-frequency distortions in the reconstructions. Here, we present a new approach that learns separately how to handle the two frequency bands, low and high, and learns how to synthesize these two bands into full-band reconstructions. We show that this "learning to synthesize" (LS) method yields phase reconstructions of high spatial resolution and without artefacts and that it is resilient to high-noise conditions, e.g
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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.