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

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 developed.
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Racemic Mixtures and the Resolution of Enantiomers02:30

Racemic Mixtures and the Resolution of Enantiomers

A racemic mixture, or racemate, is an equimolar mixture of enantiomers of a molecule that can be separated using their unique interaction with chiral molecules or media. Racemic mixtures are denoted by the (±)- prefix. This ‘optical rotation descriptor’ applies to the whole solution of a racemic mixture rather than a specific stereoisomer. Enantiomers typically have the same physical and chemical properties. Hence, they are not easily separable. However, enantiomers can exhibit different...
Chromatographic Resolution01:15

Chromatographic Resolution

In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...

You might also read

Related Articles

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

Sort by
Same author

Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.

EClinicalMedicine·2025
Same author

Euglycemic diabetic ketoacidosis associated metabolic encephalopathy caused by dapagliflozin: a rare case report.

BMC neurology·2025
Same author

Scattering spectra models for physics.

PNAS nexus·2024
Same author

Solid harmonic wavelet scattering for predictions of molecule properties.

The Journal of chemical physics·2018
Same author

Understanding deep convolutional networks.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2016
Same author

Scattering transform for intrapartum fetal heart rate variability fractal analysis: a case-control study.

IEEE transactions on bio-medical engineering·2014
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Super-resolution with sparse mixing estimators.

Stéphane Mallat, Guoshen Yu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 12, 2010
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new inverse problem estimator using adaptive linear estimators and sparse mixing weights. This method improves signal representation and achieves state-of-the-art results in adaptive directional image interpolation.

    More Related Videos

    Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection
    07:42

    Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

    Published on: February 24, 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

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
    07:12

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

    Published on: January 6, 2026

    Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection
    07:42

    Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

    Published on: February 24, 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

    Area of Science:

    • Signal processing
    • Inverse problems
    • Image interpolation

    Background:

    • Inverse problems are common in science and engineering.
    • Existing methods for signal representation and interpolation have limitations.

    Purpose of the Study:

    • To introduce a novel class of inverse problem estimators.
    • To improve sparse signal representation and adaptive directional image interpolation.

    Main Methods:

    • Mixing adaptively a family of linear estimators with different priors.
    • Calculating sparse mixing weights over coefficient blocks in a frame.
    • Minimizing an l1 norm considering signal regularity within blocks.
    • Employing an O(N logN) algorithm for adaptive directional image interpolation over a wavelet frame.

    Main Results:

    • Achieved state-of-the-art numerical results in adaptive directional image interpolation.
    • Demonstrated improved sparse signal representation through adaptive mixing weights.

    Conclusions:

    • The proposed inverse problem estimator offers a powerful approach for signal processing tasks.
    • The adaptive directional image interpolation method provides superior performance.