Mixed constrained image filter design using particle swarm optimization

Zhiguo Bao, Takahiro Watanabe

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This article describes an evolutionary image filter design for noise reduction using particle swarm optimization (PSO), where mixed constraints on the circuit complexity, power, and signal delay are optimized. First, the evaluated values of correctness, complexity, power, and signal delay are introduced to the fitness function. Then PSO autonomously synthesizes a filter. To verify the validity of our method, an image filter for noise reduction was synthesized. The performance of the resultant filter by PSO was similar to that of a genetic algorithm (GA), but the running time of PSO is 10% shorter than that of GA.

Original languageEnglish
Pages (from-to)363-368
Number of pages6
JournalArtificial Life and Robotics
Volume15
Issue number3
DOIs
Publication statusPublished - 2010

Fingerprint

Particle swarm optimization (PSO)
Noise
Noise abatement
Genetic algorithms
Networks (circuits)

Keywords

  • Evolutionary design
  • Image filter
  • PSO

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Mixed constrained image filter design using particle swarm optimization. / Bao, Zhiguo; Watanabe, Takahiro.

In: Artificial Life and Robotics, Vol. 15, No. 3, 2010, p. 363-368.

Research output: Contribution to journalArticle

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