Jove
Visualize
Contact Us

Related Concept Videos

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

13.0K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
13.0K

You might also read

Related Articles

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

Sort by
Same author

Challenges and prospects for malaria elimination in the Greater Mekong Subregion.

Acta tropica·2011
Same author

[Optimization of extraction procedure of tongmai granules by orthogonal design with pharmacodynamic index].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2011
Same author

Determinants of postoperative corneal edema and impact on goldmann intraocular pressure.

Cornea·2011
Same author

Z-palatopharyngoplasty plus genioglossus advancement and hyoid suspension for obstructive sleep apnea hypopnea syndrome.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2011
Same author

Efficient and selective photodimerization of 2-naphthalenecarbonitrile mediated by cucurbit[8]uril in an aqueous solution.

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology·2011
Same author

Quality assurance and quality improvement in U.S. clinical molecular genetic laboratories.

Current protocols in human genetics·2011
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 Experiment Video

Updated: May 24, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Practical Compact Deep Compressed Sensing.

Bin Chen, Jian Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces PCNet, a deep learning network for compressed sensing (CS) that significantly reduces sampling costs for image reconstruction. PCNet demonstrates superior accuracy and generalization, especially for high-resolution images.

    More Related Videos

    Compact Quantum Dots for Single-molecule Imaging
    17:14

    Compact Quantum Dots for Single-molecule Imaging

    Published on: October 9, 2012

    18.0K
    A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging
    05:32

    A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging

    Published on: June 21, 2017

    10.4K

    Related Experiment Videos

    Last Updated: May 24, 2025

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Compact Quantum Dots for Single-molecule Imaging
    17:14

    Compact Quantum Dots for Single-molecule Imaging

    Published on: October 9, 2012

    18.0K
    A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging
    05:32

    A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging

    Published on: June 21, 2017

    10.4K

    Area of Science:

    • Computer Vision
    • Signal Processing
    • Machine Learning

    Background:

    • Deep networks have shown success in compressed sensing (CS), reducing sampling costs.
    • CS enables significant reductions in data acquisition expenses.
    • Growing attention is given to CS for its efficiency in various applications.

    Purpose of the Study:

    • Propose PCNet, a practical and compact deep network for general image CS.
    • Design a novel collaborative sampling operator for efficient data acquisition.
    • Develop an enhanced reconstruction network for improved performance.

    Main Methods:

    • PCNet employs a collaborative sampling operator with deep conditional filtering and dual-branch fast sampling.
    • The sampling operator utilizes learned convolutions and transforms like DCT with Gaussian matrices.
    • An enhanced proximal gradient descent unrolled network facilitates image reconstruction.

    Main Results:

    • PCNet achieves superior reconstruction accuracy and generalization across natural, quantized, and self-supervised CS.
    • The network performs exceptionally well on high-resolution images.
    • A deployment-oriented scheme enables hardware integration for single-pixel CS systems.

    Conclusions:

    • PCNet offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates.
    • The proposed methods advance the field of deep learning for compressed sensing.
    • PCNet provides a practical solution for efficient image acquisition and reconstruction.