ArticleLight, science & applications2022
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization.
Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 95 citations in OpenAlex.
- Snapshot 3D image projection using a diffractive decoder.Light, science & applications · 2026Article
- Automated HER2 Scoring with Uncertainty Quantification Using Lensfree Holography and Deep Learning.BME frontiers · 2026Article
- Propagation-adaptive 4K computer-generated holography using physics-constrained spatial and Fourier neural operator.Nature communications · 2025Article
- BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images.NPJ digital medicine · 2025Article
- OAH-Net: a deep neural network for efficient and robust hologram reconstruction for off-axis digital holographic microscopy.Biomedical optics express · 2025Article
- Neural network-based processing and reconstruction of compromised biophotonic image data.Light, science & applications · 2024Review
- Holo-USensors (Basel, Switzerland) · 2024Article
- Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network.Optica · 2024Article
- Digital in-line holographic microscopy for label-free identification and tracking of biological cells.Military Medical Research · 2024Review
- All-optical complex field imaging using diffractive processors.Light, science & applications · 2024Article
- Liquid lens based holographic camera for real 3D scene hologram acquisition using end-to-end physical model-driven network.Light, science & applications · 2024Article
- Non-orthogonal optical multiplexing empowered by deep learning.Nature communications · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Randomness assisted in-line holography with deep learning.Scientific reports · 2023Article
- Review
- Classification of Holograms with 3D-CNN.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging. However, the generalization of their image reconstruction performance to new types of samples never seen by the network remains a challenge. Here we introduce a deep learning framework, termed Fourier Imager Network (FIN), that can perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples, exhibiting unprecedented success in external generalization. FIN architecture is based on spatial Fourier transform modules that process the spatial frequencies of its inputs using learnable filters and a global receptive field. Compared with existing convolutional deep neural networks used for hologram reconstruction, FIN exhibits superior generalization to new types of samples, while also being much faster in its image inference speed, completing the hologram reconstruction task in ~0.04 s per 1 mm
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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.