ArticleOptica2024
Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network.
Article in Optica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- MIMAS: An open-source microlens-array miniature microscope for accessible large-field cortical imaging in freely behaving mice.Science advances · 2026Article
- Photonics Breakthroughs 2025: Mask-Based Integrated Miniscope for Large-scale Volumetric Imaging.IEEE photonics journal · 2026Article
- Coordinate-conditioned Deconvolution for Scalable Spatially Varying High-Throughput Imaging.IEEE transactions on computational imaging · 2026Article
- DeepInMiniscope: Deep learning-powered physics-informed integrated miniscope.Science advances · 2025Article
- Propagation-adaptive 4K computer-generated holography using physics-constrained spatial and Fourier neural operator.Nature communications · 2025Article
- Lensless imaging with a programmable Fresnel zone aperture.Science advances · 2025Article
- EventLFM: event camera integrated Fourier light field microscopy for ultrafast 3D imaging.Light, science & applications · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Traditional fluorescence microscopy is constrained by inherent trade-offs among resolution, field of view, and system complexity. To navigate these challenges, we introduce a simple and low-cost computational multi-aperture miniature microscope, utilizing a microlens array for single-shot wide-field, high-resolution imaging. Addressing the challenges posed by extensive view multiplexing and non-local, shift-variant aberrations in this device, we present SV-FourierNet, a multi-channel Fourier neural network. SV-FourierNet facilitates high-resolution image reconstruction across the entire imaging field through its learned global receptive field. We establish a close relationship between the physical spatially varying point-spread functions and the network's learned effective receptive field. This ensures that SV-FourierNet has effectively encapsulated the spatially varying aberrations in our system and learned a physically meaningful function for image reconstruction. Training of SV-FourierNet is conducted entirely on a physics-based simulator. We showcase wide-field, high-resolution video reconstructions on colonies of freely moving
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.