Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Downsampling

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...

Aliasing

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...

Upsampling

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...

Discrete Fourier Transform

Discrete Fourier Transform

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...

Discrete-Time Fourier Series

Discrete-Time Fourier Series

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]...

You might also read

Related Articles

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

Sort by

Same author

Effect of Medicaid coverage of tobacco-dependence treatments on smoking cessation.

International journal of environmental research and public health·2010

Same author

Cytokine and autoantibody patterns in acute liver failure.

Journal of immunotoxicology·2009

Same author

A novel scoring system for prognostic prediction in d-galactosamine/lipopolysaccharide-induced fulminant hepatic failure BALB/c mice.

BMC gastroenterology·2009

Same author

Mammalian target of rapamycin signaling pathway contributes to glioma progression and patients' prognosis.

The Journal of surgical research·2009

Same author

Estrogen receptor neurobiology and its potential for translation into broad spectrum therapeutics for CNS disorders.

Current molecular pharmacology·2009

Same author

Transcriptional and post-translational regulation of adiponectin.

The Biochemical journal·2009

Search research articles

Related Experiment Video

Updated: Jun 26, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Spatial frequency domain information aggregation network for radar image despeckling.

Guoliang Zhu^1,2, Feng Liu^2,3, Jinjian Zhang²

¹Beijing Institute of Technology, Beijing, 100081, China.

Scientific Reports

|June 24, 2026

Summary

This study introduces SFSARNet, a novel network for radar image denoising. By integrating spatial and frequency domain analysis, it effectively reduces speckle noise in synthetic aperture radar (SAR) images.

Keywords:

Deep learning Image denoise Image enhancement SAR image despeckling

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Related Experiment Videos

Last Updated: Jun 26, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Area of Science:

Remote Sensing
Image Processing
Artificial Intelligence

Background:

Speckle noise in radar images degrades image quality and hinders applications.
Convolutional Neural Networks (CNNs) show promise in radar image despeckling but primarily operate in the spatial domain.

Purpose of the Study:

To propose a novel deep learning approach for synthetic aperture radar (SAR) image despeckling.
To investigate the benefits of combining spatial and frequency domain information for enhanced denoising.

Main Methods:

Development of the Spatial-Frequency domain Aggregation Network (SFSARNet).
SFSARNet features separate spatial and frequency domain branches for information extraction.
A dual-domain aggregation module integrates complementary information from both domains.

Main Results:

SFSARNet effectively reduces speckle noise in SAR images.
Quantitative and qualitative evaluations show superior performance compared to existing methods.
The network leverages both local structural details (spatial) and global context (frequency).

Conclusions:

Combining spatial and frequency domain analysis offers a powerful approach for SAR image despeckling.
SFSARNet demonstrates significant improvements in radar image denoising.
The proposed method provides a valuable tool for enhancing radar image applications.