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

656
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...
656
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

812
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
812
Aliasing01:18

Aliasing

731
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
731
Upsampling01:22

Upsampling

679
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...
679
Downsampling01:20

Downsampling

744
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...
744
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

413
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
413

You might also read

Related Articles

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

Sort by
Same author

Clinical profile of patients with eating disorders attending emergency services after suicide attempt.

Spanish journal of psychiatry and mental health·2026
Same author

Viscoelastic energy index and pulmonary resilience in mechanically ventilated patients: a Bayesian longitudinal multicentre study.

Medicina intensiva·2026
Same author

Modulation of Oncogenic KRAS Signaling by Branched Actin-driven Cell Membrane Protrusions.

Research square·2026
Same author

Challenges Impacting Delaware Dental Professionals' Capacity to Care for Patients with Disabilities.

Delaware journal of public health·2026
Same author

Demographic and clinical baseline characteristics from the Spanish SURVIVE prospective cohort study on suicide attempts.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same author

Modulation of Oncogenic KRAS Signaling by Branched Actin-driven Cell Membrane Protrusions.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Mar 12, 2026

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

10.4K

Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution.

Victor Perez1, Bo-Jui Chang1, Ernst Hans Karl Stelzer1

  • 1Buchmann Institute for Molecular Life Sciences (BMLS) Goethe Universität Frankfurt am Main Max-von-Laue-Strasse 15, 60438 Frankfurt am Main, Germany.

Scientific Reports
|November 17, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an objective method using Richardson-Lucy deconvolutions to reduce artifacts in structured illumination microscopy (SIM) super-resolution images. The new filtering approach improves image quality without subjective parameter tuning.

More Related Videos

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

8.4K

Related Experiment Videos

Last Updated: Mar 12, 2026

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

10.4K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

8.4K

Area of Science:

  • Optical microscopy
  • Super-resolution imaging
  • Image processing

Background:

  • Structured illumination microscopy (SIM) requires reconstruction algorithms for super-resolution images.
  • Reconstruction artifacts can compromise image quality in SIM.
  • Current methods use empirical parameter tuning, leading to subjective and unstable results.

Purpose of the Study:

  • To develop a robust and objective method for artifact reduction in 2D-SIM images.
  • To improve the quality and reliability of super-resolution reconstructions.
  • To provide a framework for identifying common SIM artifacts.

Main Methods:

  • Implemented two filtering steps based on Richardson-Lucy deconvolutions.
  • Analyzed artifacts stemming from out-of-focus background and reconstruction spectrum fluctuations.
  • Applied the method to various test specimens, including microtubules, yeast, and mammalian cells.

Main Results:

  • The proposed filtering method objectively minimizes artifacts in SIM reconstructions.
  • Demonstrated significant improvements in image quality for diverse biological samples.
  • Provided clear criteria for identifying out-of-focus background and spectral artifacts.

Conclusions:

  • The Richardson-Lucy deconvolution-based filtering offers a superior alternative to empirical tuning for SIM artifact reduction.
  • This objective approach enhances the reliability and quality of super-resolution images.
  • The artifact identification resource aids researchers in optimizing SIM data processing.