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

8.0K
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...
8.0K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

275
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...
275

You might also read

Related Articles

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

Sort by
Same author

Vibration-Induced Texture Deterioration in Kiwifruit: Molecular Mechanisms and Modulation by 1-MCP-Mediated Pectin Stabilization.

Journal of agricultural and food chemistry·2026
Same author

Urbanization, wildfire exposure, and youth mental health: a narrative review.

Current opinion in psychiatry·2026
Same author

Quantifying the evidence on associated factors for diabetes-related foot complications: An umbrella review of published systematic reviews and meta-analyses.

Diabetes research and clinical practice·2026
Same author

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same author

Synergistic Nitrogen Removal and Community Interaction Mechanism of Immobilized Bacteria Algae Symbiosis System.

Molecules (Basel, Switzerland)·2026
Same author

Comparison of procedural efficiency and safety between transradial and transfemoral approaches in elective diagnostic cerebral angiography: A retrospective cohort study.

Medicine·2026

Related Experiment Video

Updated: Jan 12, 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

8.8K

Multi-stage iterative compressed sensing framework based on DIFF transformer and ISTA for remote sensing images.

Rongrong Wang1, Yong Fang2

  • 1School of Information Engineering, Chang'an University, Shangyuan Road, Xi'an, 710064, Shaanxi, China.

Scientific Reports
|November 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-stage compressed sensing (CS) framework for remote sensing images, enhancing image reconstruction quality and reducing data storage needs. The new method shows significant improvements over existing techniques, especially under noisy conditions.

Keywords:
Compressed sensingDIFF transformerISTAImage reconstructionRemote sensing

More Related Videos

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging
08:55

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

Published on: July 12, 2022

5.7K
Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
09:09

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

10.2K

Related Experiment Videos

Last Updated: Jan 12, 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

8.8K
Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging
08:55

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

Published on: July 12, 2022

5.7K
Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
09:09

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

10.2K

Area of Science:

  • Remote Sensing
  • Image Processing
  • Signal Processing

Background:

  • Remote sensing image storage and compression demand significant memory and power.
  • Compressed sensing (CS) offers a solution but faces challenges like attention noise and gradient instability in current methods.

Purpose of the Study:

  • To propose a multi-stage iterative compressed sensing framework for remote sensing images.
  • To integrate local details and global structures while prioritizing context-related information for improved reconstruction.

Main Methods:

  • A novel framework combining a data-driven sampling module and a multi-stage iterative reconstruction module using DIFF Transformer and iterative shrinkage threshold algorithm (ISTA).
  • The sampling module utilizes a trained sensing matrix.
  • The reconstruction module employs stacked and cross-talked DIFF Transformer and ISTA for iterative refinement.

Main Results:

  • The proposed method significantly outperforms advanced techniques, showing improvements of up to 2.98 dB on the NWPU VHR-10 test set compared to ISTA-Net+.
  • The framework demonstrates robustness, being largely unaffected by added noise even at low sampling rates.

Conclusions:

  • The multi-stage iterative compressed sensing framework effectively enhances remote sensing image reconstruction.
  • The integration of DIFF Transformer and ISTA offers a promising direction for efficient and robust image compression in remote sensing applications.