Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

14.8K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
14.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unconventional atomic-level mechanism induced by a submerged saddle in the O(<sup>1</sup>D) + CH<sub>3</sub>OCH<sub>3</sub> reaction.

Communications chemistry·2026
Same author

Mindfulness, Psychological Resilience, and Social Function Deficits in Young and Middle-Aged Lymphoma Patients: A Latent Profile and Mediation Analysis.

Cancer management and research·2026
Same author

Anomalous enhancement of thermal conduction across twisted van der Waals heterointerfaces.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

High-intensity interval training induces lactate dehydrogenase B lactylation to inhibit hepatic lipogenesis to alleviate metabolic dysfunction-associated fatty liver disease.

Diabetes, obesity & metabolism·2026
Same author

Genome sequencing reveals variation of African swine fever virus in Nigerian outbreaks and identification of two major West African viral lineages.

Microbial genomics·2026
Same author

Convergent Evolution and Host-Limiting Impacts of SARS-CoV-2 Revealed by Cellular Experiments.

Molecular biology and evolution·2025
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles
  1. Home
  3. Spatially Variable Resolution Single-pixel Imaging Reconstruction Based On Diffusion Transformers.
  1. Home
  3. Spatially Variable Resolution Single-pixel Imaging Reconstruction Based On Diffusion Transformers.

Related Experiment Video

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

27.2K

Spatially variable resolution single-pixel imaging reconstruction based on diffusion transformers.

Yuming Peng, Fengyi Li, Yang Liu

    Applied Optics
    |March 17, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a novel SVR-DiT-Net for single-pixel imaging (SPI) reconstruction, enhancing image quality in critical regions under low measurements. The method uses a spatial variant resolution module and diffusion transformers for improved SPI performance.

    More Related Videos

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

    8.9K
    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    29.4K

    Related Experiment Videos

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
    17:06

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

    Published on: November 8, 2012

    27.2K
    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

    8.9K
    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    29.4K

    Area of Science:

    • Computational imaging
    • Signal processing
    • Machine learning for imaging

    Background:

    • Single-pixel imaging (SPI) relies on compressive sensing but suffers quality degradation at low measurement rates.
    • Existing SPI reconstruction methods struggle to preserve details in critical image regions with limited data.

    Purpose of the Study:

    • To develop an advanced SPI reconstruction framework that improves image fidelity, especially in visually important areas, under low measurement conditions.
    • To introduce diffusion transformers (DiTs) into SPI reconstruction for the first time, leveraging their generative capabilities.

    Main Methods:

    • A two-stage cascaded architecture, SVR-DiT-Net, is proposed.
    • A spatial variant resolution module (SVR) dynamically allocates measurement resources using progressive pixel-sharing and a prioritized differential loss function.
  • A novel image-conditioned guidance mechanism based on diffusion transformers (DiTs) is employed, using SVR's output for iterative denoising and reconstruction.

    • Main Results:

    • The SVR-DiT-Net achieves significant improvements in both global and regional image quality assessments.
    • Reconstruction quality is notably enhanced in visually critical areas, even with a limited number of measurements.
    • The framework demonstrates adjustable reconstruction quality across different image regions.

    Conclusions:

    • The proposed SVR-DiT-Net effectively addresses the challenge of SPI reconstruction quality degradation at low measurement rates.
    • The integration of diffusion transformers offers a new generative approach for high-fidelity SPI.
    • This work provides a novel perspective for generative model-based single-pixel imaging research.