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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Related Experiment Video

Updated: Jun 6, 2026

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners
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Published on: March 13, 2026

Homogeneity localization using particle filters with application to noise estimation.

Mohammed Ghazal1, Aishy Amer

  • 1Electrical and Computer Engineering Department, Concordia University, Montréal, QC, Canada. ghazal@ieee.org

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 9, 2010
PubMed
Summary

This study introduces a novel image processing technique for identifying uniform image regions and estimating additive white Gaussian noise (AWGN) variance. The method efficiently locates homogeneous areas using particle filtering, reducing computational load and improving accuracy over traditional block-based approaches.

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Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Accurate estimation of noise variance is crucial for image quality assessment and restoration.
  • Traditional block-based noise estimation methods can be computationally intensive and may not perform optimally in the presence of image structures.

Purpose of the Study:

  • To develop an efficient method for localizing image homogeneity and estimating additive white Gaussian noise (AWGN) variance.
  • To overcome the limitations of full-search block-based methods by reducing the number of homogeneity measurements required.

Main Methods:

  • Utilizes spatially scattered initial seeds and particle filtering to guide movement towards homogeneous image regions.
  • Employs a dynamic model and a homogeneity observation model based on Laplacian structure detectors for particle filter guidance.
  • Estimates AWGN variance from variances of detected homogeneous areas using an adaptive trimmed-mean robust estimator.

Main Results:

  • The proposed method significantly reduces the number of homogeneity measurements compared to block-based techniques.
  • Achieves higher accuracy in localizing homogeneity and estimating noise variance.
  • Demonstrates robustness in noise variance estimation even with reduced sample sizes.

Conclusions:

  • The particle filtering approach offers an efficient and accurate alternative for image noise analysis.
  • The method effectively balances computational cost and estimation performance.
  • This technique has potential applications in image denoising, analysis, and quality assessment.