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

Computed Tomography01:10

Computed Tomography

9.2K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
9.2K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

514
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
514

You might also read

Related Articles

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

Sort by
Same author

Wind energy-driven rabbit hair TENG for railway detection.

iScience·2026
Same author

Association between inflammatory biomarkers and depressive symptoms among diabetes mellitus and diabetic retinopathy: A cross-sectional study.

Medicine·2026
Same author

High-pressure microjet-assisted fabrication of sanxan-microalgae composite hydrogels with modified rheological and thermal properties.

International journal of biological macromolecules·2026
Same author

Contribution of DNA methylation predicted leptin to all-cause, cancer, and cardiovascular mortality among US adults aged 50 years and older: findings from NHANES 1999-2002.

Therapeutic advances in endocrinology and metabolism·2026
Same author

Comparative study of inorganic, organic, and functional calcium salts on thermal and rheological properties of sanxan hydrogels.

International journal of biological macromolecules·2026
Same author

Research on multi-cylinder coordinated coupling synchronous control strategy based on strain energy.

Scientific reports·2025

Related Experiment Video

Updated: Mar 7, 2026

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

Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing.

Chao Sun1, Baoping Wang2, Yang Fang3

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China. sunchao2013@mail.nwpu.edu.cn.

Sensors (Basel, Switzerland)
|February 7, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new compressed sensing (CS) method for synthetic aperture radar (SAR) imaging. The joint sparse recovery approach improves scatterer precision and preserves vital scattering information for target recognition.

Keywords:
compressed sensingjoint sparse recoverymultichannelsynthetic aperture radarwide-angle

More Related Videos

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

9.0K

Related Experiment Videos

Last Updated: Mar 7, 2026

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
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

9.0K

Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Science

Background:

  • Synthetic aperture radar (SAR) imaging benefits from compressed sensing (CS) theory.
  • Independent CS application to each channel neglects inter-channel information and can cause scatterer position inconsistencies.
  • This inconsistency complicates the extraction of scattering information.

Purpose of the Study:

  • To propose a novel CS-based method for joint sparse recovery of multichannel SAR images.
  • To enhance imaging performance and preserve scattering information by exploiting joint sparsity.
  • To improve scatterer recovery precision and ensure positional consistency across channels.

Main Methods:

  • Exploiting the joint sparsity of the signal ensemble from multiple SAR channels or sub-apertures.
  • Developing a novel CS-based method for joint sparse recovery of all channel images.
  • Employing a modified orthogonal matching pursuit algorithm to solve the joint sparse recovery problem.

Main Results:

  • The proposed method effectively improves the recovery precision of scatterers.
  • Valuable scattering information is preserved during the image formation process.
  • The method achieves better imaging performance with fewer measurements compared to independent CS processing.

Conclusions:

  • The joint sparse recovery method enhances multichannel SAR imaging performance.
  • It effectively preserves scattering information crucial for target recognition.
  • The approach offers superior results over independent CS processing for SAR applications.