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

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...

You might also read

Related Articles

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

Sort by
Same author

Analytical study on steady seepage of a foundation pit adjacent to a structure.

Scientific reports·2025
Same author

PKG-Mediated Phosphorylation of TOP2A Activates HDAC to Drive Photoreceptor Cell Death in rd1 Mouse Inherited Retinal Degeneration.

Journal of neurochemistry·2025
Same author

A Circularly Polarized Broadband Composite Spiral Antenna for Ground Penetrating Radar.

Sensors (Basel, Switzerland)·2025
Same author

Fabricating a Three-Dimensional Surface-Enhanced Raman Scattering Substrate Using Hydrogel-Loaded Freeze-Induced Silver Nanoparticle Aggregates for the Highly Sensitive Detection of Organic Pollutants in Seawater.

Sensors (Basel, Switzerland)·2025
Same author

Investigating immune cell infiltration and gene expression features in pterygium pathogenesis.

Scientific reports·2025
Same author

Clinical characteristics and risk factors for readmission after deep anterior lamellar keratoplasty: a nationwide, cross-sectional, multicenter study.

BMC ophthalmology·2025
Same journal

Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

Optics letters·2026
Same journal

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

Optics letters·2026
Same journal

Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

Optics letters·2026
Same journal

Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

Optics letters·2026
Same journal

Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

Optics letters·2026
Same journal

Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

Optics letters·2026
See all related articles

Related Experiment Video

Updated: May 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

Blind Poissonian images deconvolution with framelet regularization.

Houzhang Fang1, Luxin Yan, Hai Liu

  • 1Science and Technology on Multi-spectral Information Processing Laboratory, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.

Optics Letters
|March 5, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new blind deconvolution method using framelet and total variation (TV) regularization. The approach effectively reduces noise while preserving image details and edges.

More Related Videos

Light Sheet Microscopy Imaging and Mounting Strategies for Early Zebrafish Embryos
08:33

Light Sheet Microscopy Imaging and Mounting Strategies for Early Zebrafish Embryos

Published on: July 19, 2024

Related Experiment Videos

Last Updated: May 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

Light Sheet Microscopy Imaging and Mounting Strategies for Early Zebrafish Embryos
08:33

Light Sheet Microscopy Imaging and Mounting Strategies for Early Zebrafish Embryos

Published on: July 19, 2024

Area of Science:

  • Image processing
  • Computational imaging
  • Applied mathematics

Background:

  • Blind deconvolution is crucial for image restoration.
  • Existing methods struggle with noise suppression and detail recovery.

Purpose of the Study:

  • To develop an advanced blind deconvolution algorithm.
  • To improve noise reduction and edge preservation in degraded images.

Main Methods:

  • Maximum a posteriori (MAP) blind deconvolution.
  • Framelet regularization for image.
  • Total variation (TV) regularization for the point spread function (PSF).
  • Split Bregman method for optimization.

Main Results:

  • The proposed method effectively suppresses noise.
  • Enhanced recovery of edges and fine details compared to TV-based methods.
  • Successful application to both simulated and real-world images.

Conclusions:

  • The framelet and TV regularized MAP approach offers superior performance in blind deconvolution.
  • The split Bregman method provides an efficient solution for the optimization problem.