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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Downsampling01:20

Downsampling

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...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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...

You might also read

Related Articles

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

Sort by
Same author

Intra-Sylvian versus intracerebral haematoma associated with ruptured middle cerebral artery aneurysm: initial and follow-up imaging features.

Clinical radiology·2025
Same author

Acoustic Tweezers for Microscopy of Living Organisms.

IEEE transactions on bio-medical engineering·2025
Same author

Subcutaneous tunnelling versus conventional insertion of peripherally inserted central catheters in hospitalized patients (TUNNEL-PICC): a multi-centre, open-label, randomized, controlled trial.

The Journal of hospital infection·2024
Same author

Prescription of oral antibiotics and its appropriateness for outpatients in a tertiary care hospital in Korea.

The Journal of hospital infection·2024
Same author

Disease burden of bacteraemia with extended-spectrum beta-lactamase-producing and carbapenem-resistant Enterobacterales in Korea.

The Journal of hospital infection·2023
Same author

Development of Allison scanner to measure transverse emittance at low energy beam transport of rare-isotope accelerator complex for ON-line experiments.

The Review of scientific instruments·2023

Related Experiment Video

Updated: Jul 7, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Image enhancement based on signal subspace approach.

K S Lee, E S Kim, W Doh

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 13, 2008
    PubMed
    Summary

    This study introduces a block-by-block image enhancement algorithm using signal subspace methods to remove uncorrelated additive noise. The method effectively separates and removes noise, improving image quality by reconstructing the clean image from signal components.

    More Related Videos

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    Related Experiment Videos

    Last Updated: Jul 7, 2026

    A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
    10:37

    A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

    Published on: August 22, 2025

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    Area of Science:

    • Digital Image Processing
    • Signal Processing
    • Machine Learning

    Background:

    • Additive noise significantly degrades image quality, posing challenges in various applications.
    • Traditional image enhancement techniques often struggle with uncorrelated noise.
    • Subspace methods offer a promising approach for noise reduction in digital images.

    Discussion:

    • The proposed algorithm processes images block-by-block, enabling efficient handling of large datasets.
    • The core of the method involves distinguishing signal and noise subspaces.
    • Noise components are identified and eliminated within the noise subspace.

    Key Insights:

    • The signal subspace method effectively isolates image signal components from noise.
    • Estimating the clean image from the signal subspace yields enhanced image fidelity.
    • This approach demonstrates superior performance in removing uncorrelated additive noise compared to conventional methods.

    Outlook:

    • Future work could explore adaptive block sizes for varied noise levels.
    • Investigating the algorithm's performance on different types of noise is warranted.
    • Potential applications include medical imaging, satellite imagery, and surveillance systems.