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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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 sampling...
Aliasing01:18

Aliasing

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 signal...
Fast Fourier Transform01:10

Fast Fourier Transform

The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
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.

You might also read

Related Articles

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

Sort by
Same author

High-temporal-contrast stretcher design based on an asymmetric Offner triplet.

Optics express·2025
Same author

Analytic phase solutions of three-wave interactions.

Optics letters·2021
Same author

Gaussian curvature and stigmatic imaging relations for the design of an unobscured reflective relay.

Optics letters·2018
Same author

Chromatic diversity: a new approach for characterizing spatiotemporal coupling of ultrashort pulses.

Optics express·2018
Same author

Chromatic-aberration diagnostic based on a spectrally resolved lateral-shearing interferometer.

Applied optics·2016
Same author

Offner radial group delay compensator for ultra-broadband laser beam transport.

Optics letters·2014

Related Experiment Video

Updated: May 28, 2026

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Highly accurate wavefront reconstruction algorithms over broad spatial-frequency bandwidth.

Seung-Whan Bahk1

  • 1Laboratory for Laser Energetics,University of Rochester, 250 East River Road, Rochester, New York 14623, USA. sbah@lle.rochester.edu

Optics Express
|October 15, 2011
PubMed
Summary
This summary is machine-generated.

New wavefront reconstruction algorithms offer high accuracy using frequency domain analysis. A novel Simpson rule-based method improves performance, achieving up to 85% bandwidth accuracy for slopes data.

More Related Videos

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Related Experiment Videos

Last Updated: May 28, 2026

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Area of Science:

  • Optics and Photonics
  • Computational Science

Background:

  • Wavefront reconstruction from slopes data is crucial in adaptive optics and optical testing.
  • Existing algorithms face limitations in accuracy and spatial-frequency bandwidth.

Purpose of the Study:

  • To develop novel algorithms for accurate wavefront reconstruction.
  • To enhance understanding of frequency response and noise propagation in reconstructors.
  • To improve the spatial-frequency bandwidth of wavefront reconstruction.

Main Methods:

  • Analysis of wavefront reconstructors in the frequency domain using discrete band-limited signal tools.
  • Development of a new spatial-domain iterative reconstruction algorithm based on the Simpson rule (averaging over 8 neighbors).
  • Adaptation of a frequency-domain algorithm from rectangular to hexagonal geometry.

Main Results:

  • The Simpson rule-based reconstructor achieves high accuracy up to 85% of the bandwidth.
  • Frequency domain analysis provides insights into improving frequency response and noise propagation.
  • Hexagonal geometry adaptation increases flexibility for frequency-domain algorithms.

Conclusions:

  • The developed algorithms significantly improve wavefront reconstruction accuracy and bandwidth.
  • Frequency domain analysis is a powerful tool for optimizing reconstructor performance.
  • The Simpson rule-based method offers a substantial advancement in reconstructor accuracy.