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

Reducing Line Loss01:18

Reducing Line Loss

216
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...
216
Deconvolution01:20

Deconvolution

293
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
293
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

482
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...
482
Convolution Properties II01:17

Convolution Properties II

315
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
315
Upsampling01:22

Upsampling

352
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...
352
Convolution Properties I01:20

Convolution Properties I

276
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
276

You might also read

Related Articles

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

Sort by
Same author

Effects of glucose and insulin on HepG2-C3A cell metabolism.

Biotechnology and bioengineering·2010
Same author

Overexpression of antioxidant enzymes upregulates aryl hydrocarbon receptor expression via increased Sp1 DNA-binding activity.

Free radical biology & medicine·2010
Same author

Impact of hepatitis C viral replication on CD4+ T-lymphocyte progression in HIV-HCV coinfection before and after antiretroviral therapy.

AIDS (London, England)·2010
Same author

[Expression and diagnostic significance of CD34 in brain tumors of patients with refractory epilepsy].

Zhonghua bing li xue za zhi = Chinese journal of pathology·2010
Same author

Characterization of pore-expanded amino-functionalized mesoporous silicas directly synthesized with dimethyldecylamine and its application for decolorization of sulphonated azo dyes.

Journal of hazardous materials·2010
Same author

Human leukocyte antigen-G (HLA-G) expression in cervical lesions: association with cancer progression, HPV 16/18 infection, and host immune response.

Reproductive sciences (Thousand Oaks, Calif.)·2010

Related Experiment Video

Updated: Oct 13, 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

682

A Feature-Enriched Deep Convolutional Neural Network for JPEG Image Compression Artifacts Reduction and its

Honggang Chen, Xiaohai He, Hong Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |November 18, 2021
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning model, FeCarNet, effectively reduces compression artifacts in images. This advanced network enhances image quality after lossy compression, outperforming existing methods and showing versatility in computer vision tasks.

    Related Experiment Videos

    Last Updated: Oct 13, 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

    682

    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Rapid increase in multimedia data strains storage and transmission.
    • Lossy compression (e.g., JPEG) reduces data size but introduces artifacts like blocking and blurring.
    • Existing artifact reduction methods struggle with high compression ratios.

    Purpose of the Study:

    • Introduce FeCarNet, a novel deep convolutional neural network for compression artifact reduction.
    • Enhance feature representation and fusion for superior artifact removal.
    • Improve model efficiency in terms of parameters and computation.

    Main Methods:

    • Utilized a dense network backbone enriched with multi-scale dilated and 1x1 convolutions.
    • Developed an attention-based fusion block for local and global feature integration.
    • Incorporated multi-level residual connections and pixel-shuffle layers for efficient training and enlarged receptive fields.

    Main Results:

    • FeCarNet demonstrated superior performance in artifact restoration compared to state-of-the-art methods.
    • Achieved lower model complexity (parameters and computation cost).
    • Showcased effectiveness in diverse computer vision applications like deblurring and object detection.

    Conclusions:

    • FeCarNet offers a powerful and efficient solution for reducing compression artifacts.
    • The network's architecture effectively leverages multi-scale features and residual learning.
    • FeCarNet's adaptability extends its utility beyond artifact reduction to other vision tasks.