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Color image enhancement based on nonparametric density estimation

  • Aristotle University of Thessaloniki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper presents a new filtering scheme for the removal of impulsive noise in color images. It is based on estimating the probability density function for color pixels in a filtering window by means of the kernel density estimation method. The filtering algorithm itself is based on the comparison of pixels with their neighborhood in a sliding filter window. The quality of noise suppression and detail preservation of the new filter is measured quantitatively in terms of the standard image quality criteria. The filtering results obtained with the new filter show its excellent ability to reduce noise while simultaneously preserving fine image details.

Original languageEnglish
Title of host publicationProceedings EC-VIP-MC 2003 - 4th EURASIP Conference Focused on Video / Image Processing and Multimedia Communications
EditorsMislav Grgic, Sonja Grgic
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages271-276
Number of pages6
ISBN (Electronic)9531840547, 9789531840545
DOIs
Publication statusPublished - 2003
Event4th EURASIP Conference Focused on Video / Image Processing and Multimedia Communications, EC-VIP-MC 2003 - Zagreb, Croatia
Duration: 2 Jul 20035 Jul 2003

Publication series

NameProceedings EC-VIP-MC 2003 - 4th EURASIP Conference Focused on Video / Image Processing and Multimedia Communications
Volume1

Conference

Conference4th EURASIP Conference Focused on Video / Image Processing and Multimedia Communications, EC-VIP-MC 2003
Country/TerritoryCroatia
CityZagreb
Period2/07/035/07/03

Keywords

  • Color
  • Colored noise
  • Filtering algorithms
  • Filters
  • Image quality
  • Kernel
  • Measurement standards
  • Noise measurement
  • Noise reduction
  • Probability density function

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Communication

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