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

Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...

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...

Upsampling

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...

Boundary Conditions: Lossless Lines

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...

Region of Convergence of Laplace Tarnsform

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...

Linear Approximations

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

You might also read

Related Articles

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

Sort by

Same author

Genetic variation within the human papillomavirus type 16 genome is associated with oropharyngeal cancer prognosis.

Annals of oncology : official journal of the European Society for Medical Oncology·2022

Same author

Adherence to Daily Oral TDF/FTC for PrEP in Community Health Center Populations: The Sustainable Health Center Implementation PrEP Pilot (SHIPP) Study.

AIDS and behavior·2021

Same author

Associations between oral health and general health: a surveywide association study of the NHANES.

Community dental health·2021

Same author

A potential role for protein O-fucosylation during pollen-pistil interactions.

Plant signaling & behavior·2018

Same author

Effect of age, sex, and race on selenium status of healthy residents of Augusta, Georgia.

Biological trace element research·2013

Same author

Rapid estimation of biochemical oxygen demand.

Applied biochemistry and biotechnology·2013

Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026

Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026

Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026

Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026

Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026

Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Closed form bandlimited image extrapolation.

D K Smith, R J Marks Ii

|March 25, 2010

Summary

This summary is machine-generated.

This study presents digital image extrapolation algorithms. Extrapolation accuracy is highest for image regions closest to the known data, requiring high signal-to-noise ratios for noisy images.

More Related Videos

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

Related Experiment Videos

Last Updated: Jun 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

Area of Science:

Digital Signal Processing
Image Reconstruction
Computational Imaging

Background:

Image extrapolation extends known image data to unknown regions.
Bandlimited signal extrapolation is crucial for reconstructing incomplete or corrupted images.
Digital implementations are essential for practical applications.

Purpose of the Study:

To present digital implementations of a 2-D bandlimited image extrapolation algorithm.
To evaluate the algorithm's performance on elementary target images.
To assess the impact of noise on image extrapolation accuracy.

Main Methods:

Developed digital implementations of a closed-form 2-D bandlimited image extrapolation algorithm.
Applied the algorithm to various elementary target images.
Quantified performance using figures of merit.
Investigated extrapolation of truncated images corrupted by white Gaussian noise.

Main Results:

Empirical verification that image extrapolation accuracy decreases with distance from known image regions.
Demonstrated the algorithm's effectiveness on tested elementary images.
Determined a high signal-to-noise ratio (SNR) requirement, approximately 10^10, for extrapolating noisy images.

Conclusions:

The spatial locality of information is critical for accurate image extrapolation.
Digital implementations provide a viable method for 2-D bandlimited image extrapolation.
Significant signal integrity is necessary for reliable extrapolation in the presence of noise.