ArticleOptics express2022
Learned lensless 3D camera.
Article in Optics express, 2022. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Photonics Breakthroughs 2025: Mask-Based Integrated Miniscope for Large-scale Volumetric Imaging.IEEE photonics journal · 2026Article
- IE-GADCI: An End-to-End Incoherence-Enhanced Generative Adversarial Deep Compressive Imaging.IEEE transactions on computational imaging · 2026Article
- DeepInMiniscope: Deep learning-powered physics-informed integrated miniscope.Science advances · 2025Article
- Lensless camera: Unraveling the breakthroughs and prospects.Fundamental research · 2025Review
- Lensless imaging with a programmable Fresnel zone aperture.Science advances · 2025Article
- Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network.Optica · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Single-shot three-dimensional (3D) imaging with compact device footprint, high imaging quality, and fast processing speed is challenging in computational imaging. Mask-based lensless imagers, which replace the bulky optics with customized thin optical masks, are portable and lightweight, and can recover 3D object from a snap-shot image. Existing lensless imaging typically requires extensive calibration of its point spread function and heavy computational resources to reconstruct the object. Here we overcome these challenges and demonstrate a compact and learnable lensless 3D camera for real-time photorealistic imaging. We custom designed and fabricated the optical phase mask with an optimized spatial frequency support and axial resolving ability. We developed a simple and robust physics-aware deep learning model with adversarial learning module for real-time depth-resolved photorealistic reconstructions. Our lensless imager does not require calibrating the point spread function and has the capability to resolve depth and "see-through" opaque obstacles to image features being blocked, enabling broad applications in computational imaging.
Identifiers
What OpenQuestion holds
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.