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

Downsampling01:20

Downsampling

755
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...
755
Upsampling01:22

Upsampling

688
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...
688
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

822
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...
822
Aliasing01:18

Aliasing

757
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...
757
Sampling Theorem01:15

Sampling Theorem

1.5K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.5K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

816
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
816

You might also read

Related Articles

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

Sort by
Same author

[Neurobiology of pair-bonded sociality in prairie voles].

Nihon yakurigaku zasshi. Folia pharmacologica Japonica·2026
Same author

Lipopolysaccharide-Induced Changes in Facial Expression and Orofacial Movement in Mice.

Biological & pharmaceutical bulletin·2026
Same author

Arecoline Facilitates Object Recognition in Mice.

Biological & pharmaceutical bulletin·2026
Same author

Rice-memolin modulates prefrontal neural oscillations during sleep.

Journal of pharmacological sciences·2026
Same author

Structured spontaneous activity through delta oscillation-based discrete states in the medaka telencephalon.

Scientific reports·2026
Same author

Magnesium Deficiency Disrupts Blood Glucose Homeostasis in a Time-of-Day-Dependent Manner.

Biological & pharmaceutical bulletin·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
07:47

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

7.6K

A Computationally Efficient Filter for Reducing Shot Noise in Low S/N Data.

Mami Okada1, Tomoe Ishikawa1, Yuji Ikegaya1,2

  • 1Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan.

Plos One
|June 16, 2016
PubMed
Summary
This summary is machine-generated.

A new Okada filter effectively reduces shot noise in functional multineuron calcium imaging (fMCI) and other data. This method significantly enhances signal-to-noise ratios under low signal conditions, improving data quality.

More Related Videos

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.9K

Related Experiment Videos

Last Updated: Mar 19, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
07:47

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

7.6K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.9K

Area of Science:

  • Neuroscience
  • Biophysics
  • Image Processing

Background:

  • Functional multineuron calcium imaging (fMCI) enables simultaneous recording of neuronal activity in large populations.
  • fMCI data frequently exhibits low signal-to-noise ratios (S/N) primarily due to shot noise from photon detectors.

Purpose of the Study:

  • To introduce a novel denoising procedure, the Okada filter, specifically designed to mitigate shot noise in low S/N environments like fMCI.
  • To evaluate the efficacy of the Okada filter in improving S/N ratios for fMCI data.

Main Methods:

  • The Okada filter replaces a fluorescence intensity value with the average of preceding and following frame values, unless it's the median.
  • This iterative process is applied serially to time-series data.
  • The filter's mathematical foundation is a single, continuous, differentiable logistic function-based equation.

Main Results:

  • Application of the Okada filter to fMCI data from hippocampal neurons demonstrated rapid background noise reduction.
  • A significant improvement in S/N ratios was observed after applying the Okada filter.
  • The filter proved effective for denoising electrophysiological data and photographs as well.

Conclusions:

  • The Okada filter is a mathematically tractable and effective tool for reducing shot noise in fMCI.
  • This method substantially enhances data quality in low S/N imaging and other scientific data types.
  • The filter's applicability extends beyond neuroscience, offering broad utility in scientific image and signal processing.