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

324
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...
324
Upsampling01:22

Upsampling

364
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...
364
Reducing Line Loss01:18

Reducing Line Loss

223
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
223
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

416
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...
416
Aliasing01:18

Aliasing

299
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...
299
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Underwater Image Enhancement Based on Histogram-Equalization Approximation Using Physics-Based Dichromatic Modeling.

Sensors (Basel, Switzerland)·2022
Same author

A Fast Two-Stage Bilateral Filter Using Constant Time <i>O</i>(1) Histogram Generation.

Sensors (Basel, Switzerland)·2022
Same author

Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction.

Sensors (Basel, Switzerland)·2022
Same author

Reduced-intensity conditioning therapy with fludarabine, idarubicin, busulfan and cytarabine for allogeneic hematopoietic stem cell transplantation in acute myeloid leukemia and myelodysplastic syndrome.

Leukemia research·2013
Same author

Effects of 3, 5, 3'-triiodothyronine (t3) and follicle stimulating hormone on apoptosis and proliferation of rat ovarian granulosa cells.

The Chinese journal of physiology·2013
Same author

Cardiogenic shock from acute ST-segment elevation myocardial infarction induced by severe multivessel coronary vasospasm.

European heart journal·2013
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

708

An Advanced Noise Reduction and Edge Enhancement Algorithm.

Shih-Chia Huang1, Quoc-Viet Hoang2,3, Trung-Hieu Le1,3

  • 1Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for image denoising, enhancing image quality in low-light conditions using deep learning and image fusion techniques. The approach effectively reduces noise while preserving details for natural-looking results.

Keywords:
contrast enhancementdeep image prioredge enhancementnoise removal

More Related Videos

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.4K
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

748

Related Experiment Videos

Last Updated: Oct 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

708
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.4K
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

748

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Complementary metal-oxide-semiconductor (CMOS) image sensors often introduce noise, particularly in low-illumination environments.
  • Existing image denoising methods struggle to produce natural and high-quality results.

Purpose of the Study:

  • To develop a novel approach for robust image denoising.
  • To enhance image quality and preserve details in noisy images, especially from CMOS sensors.

Main Methods:

  • A deep image prior-based module for noise reduction and contrast enhancement.
  • An image fusion (IF) module utilizing Laplacian pyramid decomposition to combine enhanced images.
  • A progressive refinement (PR) module employing summed-area tables for edge and quality improvement.

Main Results:

  • The proposed method effectively reduces noise while enhancing image contrast.
  • Image fusion prevents noise amplification and color shifts.
  • Progressive refinement improves edge details and overall image quality.

Conclusions:

  • The novel approach demonstrates superior performance in image denoising.
  • The method is efficient, robust, and produces natural, high-quality denoised images.