Jove
Visualize
Contact Us

Related Concept Videos

Discrete Fourier Transform01:15

Discrete Fourier Transform

603
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
603
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

489
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
489
Downsampling01:20

Downsampling

453
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...
453
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

786
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
786
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

628
The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the...
628
Properties of DTFT II01:24

Properties of DTFT II

381
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
381

You might also read

Related Articles

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

Sort by
Same author

Graph Neural Network-Based GrUNet and Attention Transformer Adjacency Matrix for Video Denoising.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Scalability Analysis of LoRa and Sigfox in Congested Environment and Calculation of Optimum Number of Nodes.

Sensors (Basel, Switzerland)·2024
Same author

Linear antenna array optimization using flower pollination algorithm.

SpringerPlus·2016
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles
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 Experiment Video

Updated: Nov 21, 2025

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

3.9K

Digital Image Noise Estimation Using DWT Coefficients.

Varad A Pimpalkhute, Rutvik Page, Ashwin Kothari

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 13, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new algorithm for estimating Gaussian noise strength in images. By combining Discrete Wavelet Transform (DWT) with edge detection, it accurately measures noise levels for better image processing.

    More Related Videos

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.9K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.3K

    Related Experiment Videos

    Last Updated: Nov 21, 2025

    Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
    06:37

    Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

    Published on: June 15, 2022

    3.9K
    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.9K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.3K

    Area of Science:

    • Digital Image Processing
    • Signal Analysis

    Background:

    • Accurate noise estimation is crucial for image processing tasks like denoising and compression.
    • Existing methods often rely on transform or spatial domain information, with varying degrees of success.

    Purpose of the Study:

    • To develop a robust and accurate algorithm for estimating Gaussian noise strength in digital images.
    • To improve upon existing noise estimation techniques by incorporating edge information removal.

    Main Methods:

    • A hybrid approach combining Discrete Wavelet Transform (DWT) and a Sobel edge detector.
    • Exclusion of wavelet coefficients corresponding to image edges to refine noise estimate.
    • Enhancement of accuracy through polynomial regression and mathematical validation using Parseval's theorem.

    Main Results:

    • The proposed algorithm demonstrates superior performance in estimating Gaussian noise strength.
    • Benchmarking on the LIVE image dataset shows significant outperformance compared to state-of-the-art methods.
    • Effective noise estimation across a wide range of noise levels.

    Conclusions:

    • The hybrid DWT and edge removal algorithm offers a highly accurate method for Gaussian noise strength estimation.
    • This approach provides a significant advancement for various image processing applications.
    • The method's effectiveness is validated by its strong performance on standard image datasets.