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...
Upsampling01:22

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...
Basic Operations on Signals01:22

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.
Nuclear Overhauser Enhancement (NOE)01:06

Nuclear Overhauser Enhancement (NOE)

Irradiation of a spin-active nucleus causes an increase or decrease in the signal intensity of neighboring nuclei that are not necessarily chemically bonded or involved in J-coupling. This phenomenon, called the nuclear Overhauser enhancement (NOE), results from through-space interactions between the nuclear spins. The NOE effect decreases with increasing internuclear distance and is generally not observed beyond 4 angstroms. In NOE, dipole-dipole interactions between neighboring spin-active...
Transformations of Functions III01:20

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...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

You might also read

Related Articles

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

Sort by
Same author

Automated FerryBox monitoring reveals the first recorded river induced crude oil seep transport to the Strait of Magellan in southern Patagonia.

The Science of the total environment·2024
Same author

Global taxonomic, functional, and phylogenetic diversity of bees in apple orchards.

The Science of the total environment·2023
Same author

Nonlinear confocal positioner for micron-scale target alignment.

Optics express·2023
Same author

Effects of bisphenol F, bisphenol S, and bisphenol AF on cultured human osteoblasts.

Archives of toxicology·2023
Same author

Direct Measurement of Resonances in ^{7}Be(α,γ)^{11}C Relevant to νp-Process Nucleosynthesis.

Physical review letters·2022
Same author

High accuracy astigmatic-focusing system for laser targets.

Applied optics·2022
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

Related Experiment Video

Updated: Jun 12, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Image enhancement by nonlinear signal processing.

B Javidi, J Ruiz, C Ruiz

    Applied Optics
    |June 26, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a nonlinear joint transform processor for image enhancement. The technique effectively enhances image details by modifying the Fourier magnitude, offering improved light efficiency over traditional methods.

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    Area of Science:

    • Optics and Photonics
    • Image Processing

    Background:

    • Traditional image enhancement techniques often struggle with preserving fine details.
    • Joint transform processors offer a framework for optical image processing.

    Purpose of the Study:

    • To develop and evaluate a nonlinear joint transform processor for enhanced image detail.
    • To investigate the impact of various nonlinearities on image enhancement outcomes.

    Main Methods:

    • A nonlinear joint transform processor was designed and implemented.
    • Nonlinear transformations were applied to the joint power spectrum.
    • Analytical expressions for enhanced images were derived.
    • Computer simulations were conducted to assess system performance.

    Main Results:

    • The nonlinear technique successfully enhanced image details, particularly fine features.
    • Compression-type nonlinearities redistributed energy to higher spatial frequencies.
    • The processor demonstrated superior light efficiency compared to block spatial filtering.
    • Analytical expressions provided a theoretical basis for observed enhancements.

    Conclusions:

    • Nonlinear joint transform processing is an effective method for image enhancement.
    • The technique offers significant improvements in light efficiency and detail preservation.
    • This approach provides a viable alternative to existing spatial filtering methods.