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

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Differential Leveling01:12

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...

You might also read

Related Articles

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

Sort by
Same author

Normative T<sub>1</sub> and T<sub>2</sub> Brain Atlases Across the Adult Lifespan in a Chinese Cohort: Multicenter Quantitative MRI Benchmarks for Ageing and Neurodegenerative Research.

Human brain mapping·2026
Same author

Introducing a translationally relevant mouse model of radiosurgery-induced unilateral hearing loss.

Frontiers in neuroscience·2026
Same author

How Much Does Motion Matter? Evaluating the Motion Robustness of pTx Pulses at 7 T.

Magnetic resonance in medicine·2026
Same author

Combined caLculation of Ultra-high field Biases (CLUB) With Sandwich: Fast, Simultaneous Estimation of 3D B<sub>0</sub> and Multi-Channel B<sub>1</sub> <sup>+</sup> Maps at 7 T.

Magnetic resonance in medicine·2026
Same author

Spatially regularized super-resolved constrained spherical deconvolution (SR<sup>2</sup>-CSD) of diffusion MRI data.

NeuroImage·2025
Same author

Deep Learning for fODF Estimation in Infant Brains: Model Comparison, Ground-Truth Impact, and Domain Shift Mitigation.

Human brain mapping·2025

Related Experiment Video

Updated: May 12, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Enhanced compressed sensing recovery with level set normals.

Virginia Estellers1, Jean-Philippe Thiran, Xavier Bresson

  • 1Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland. virginia.estellers@epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 27, 2013
PubMed
Summary

This study introduces a new compressive sensing algorithm using image geometry for high-quality image recovery from minimal data. The method enhances image reconstruction and detail, outperforming existing techniques in quality and robustness.

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Related Experiment Videos

Last Updated: May 12, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Area of Science:

  • Image processing
  • Computer vision
  • Applied mathematics

Background:

  • Compressive sensing (CS) enables signal recovery from fewer measurements than traditional methods.
  • Image reconstruction from limited data remains a challenge, especially for textured or noisy images.
  • Exploiting image properties can improve reconstruction quality and reduce measurement requirements.

Purpose of the Study:

  • To develop a novel compressive sensing algorithm for high-quality image reconstruction.
  • To leverage geometric image properties for enhanced recovery from sparse measurements.
  • To improve robustness against noise and expand applicability to textured images.

Main Methods:

  • Image reconstruction via iterative estimation of normal vectors of image level curves.
  • Incorporation of normal vectors, CS measurements, and sparsity constraints.
  • Extension to nonlocal operators and graphs for textured image analysis.
  • Solution of convex minimization problems using variable splitting and augmented Lagrangian methods.

Main Results:

  • Achieved high-quality image reconstruction from few measurements.
  • Demonstrated improved performance over state-of-the-art algorithms.
  • Showcased robustness to noise and various image types.
  • Successfully recovered fine detail structures in textured images.

Conclusions:

  • The proposed compressive sensing algorithm effectively utilizes image geometry for superior reconstruction.
  • The method offers enhanced detail recovery and robustness, outperforming existing approaches.
  • The algorithm's efficiency and ease of implementation make it a practical solution for sparse data image recovery.