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Related Concept Videos

Downsampling01:20

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...
Deconvolution01:20

Deconvolution

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...

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Related Experiment Video

Updated: Jun 22, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
08:44

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

Wavelet-based SAR image despeckling and information extraction, using particle filter.

Dusan Gleich1, Mihai Datcu

  • 1University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova 17, 2000 Maribor, Slovenia. dusan.gleich@uni-mb.si

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 29, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces novel wavelet-based methods for synthetic aperture radar (SAR) image despeckling using sequential Monte Carlo. The proposed algorithms effectively reduce noise while preserving essential image textures.

Related Experiment Videos

Last Updated: Jun 22, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
08:44

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

Area of Science:

  • Remote Sensing
  • Signal Processing
  • Computer Vision

Background:

  • Synthetic Aperture Radar (SAR) images are susceptible to multiplicative noise (speckle), degrading image quality and hindering analysis.
  • Traditional despeckling methods often struggle to balance noise reduction with the preservation of fine image details and textures.

Purpose of the Study:

  • To develop and evaluate new wavelet-based algorithms for SAR image despeckling.
  • To propose a model-based Bayesian approach utilizing sequential Monte Carlo methods.
  • To compare the performance of the proposed methods against existing state-of-the-art techniques.

Main Methods:

  • Two methods are presented: WGGPF (Generalized Gaussian prior) and WGMPF (Generalized Gaussian Markov Random Field prior).
  • A particle filter is employed for drawing particles from the prior distributions.
  • Texture parameters are estimated and optimized within the WGMPF framework for improved results.

Main Results:

  • Both proposed algorithms demonstrate efficient noise removal in synthetic and real SAR data.
  • The methods show comparable performance to state-of-the-art techniques based on objective measurements.
  • The WGMPF method particularly excels at preserving textures in high-resolution SAR images.

Conclusions:

  • The proposed wavelet-based despeckling algorithms offer an effective solution for SAR image noise reduction.
  • The WGMPF method provides a robust approach for preserving image textures, crucial for high-resolution SAR applications.
  • The study validates the efficacy of Bayesian and sequential Monte Carlo approaches in SAR image processing.