Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reconstruction of Signal using Interpolation

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...

Transformations of Functions II

Transformations of Functions II

Transformations in mathematics alter the position or orientation of a function’s graph while preserving its fundamental shape. One important type of transformation is the horizontal shift, which involves modifying the input variable within a function’s equation. This operation affects where outputs occur along the horizontal axis but does not alter the function’s overall structure.A horizontal shift is achieved by replacing the input variable x with either x + c or x - c, where c is a constant.

Transformations of Functions III

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...

Transformations of Functions I

Transformations of Functions I

A function's graph can be modified by changing its position or size without altering its overall shape. These transformations allow the graph to be moved across the coordinate plane while preserving its pattern and structure. One of the most common transformations is shifting, which repositions the graph without distorting it.When the output of a function is adjusted by adding or subtracting a constant, the graph shifts vertically. A positive value moves the graph upward, while a negative value...

Basic Operations on Signals

Basic Operations on Signals

Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.

You might also read

Related Articles

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

Sort by

Same author

Strehl ratio and visibility in long-baseline stellar interferometry.

Optics letters·2009

Same author

Binary-intensity ratios by the fork algorithm.

Optics letters·2009

Same author

Effect of nonisoplanaticity on the shift-and-add algorithm.

Optics letters·2009

Same author

Nontraumatic aortic perforations into gastrointestinal tract; review of the literature and report of an unusual case.

American heart journal·1950

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

Image restoration by the shift-and-add algorithm.

|September 3, 2009

Summary

A novel image restoration technique using the shift-and-add (SAA) algorithm offers speed and simplicity. This method effectively recovers objects by decorrelating a nonlinear SAA pattern, demonstrated on the Betelgeuse chromosphere.

Area of Science:

Astronomy and Astrophysics
Image Processing
Computational Science

Background:

Image restoration is crucial for analyzing astronomical data.
Existing methods may lack efficiency or simplicity for complex datasets.

Purpose of the Study:

To introduce a new, efficient image restoration method.
To apply this technique to astronomical imaging, specifically the Betelgeuse chromosphere.

Main Methods:

Developed a novel image restoration technique based on the shift-and-add (SAA) algorithm.
Utilized nonlinear correlation to create an SAA pattern weighted toward brighter pixels.
Employed a successive substitutions method, similar to Fienup's algorithm, for decorrelation and object recovery.

More Related Videos

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

Related Experiment Videos

Last Updated: Jun 20, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

Main Results:

The new SAA-based method demonstrates significant speed and simplicity.
Successfully applied the technique for image restoration of the extended chromosphere of Betelgeuse.
The decorrelation process effectively recovers the original object from the SAA pattern.

Conclusions:

The shift-and-add algorithm provides an effective and efficient approach to image restoration.
This method holds promise for analyzing extended astronomical objects like stellar chromospheres.
The technique's speed and simplicity make it a valuable tool for astronomical image processing.