Application of wavelet transform and genetic algorithms for image processing

Artem A. Belousov, Vladimir G. Spitsyn, Dmitry V. Sidorov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Getting clear high detailed images with high contrast is an important task in many spheres of science and engineering. However, it's not always possible because of imperfection of devices or environment conditions. This all leaded to development of different methods of image enhancement. In this article a developed two-phase full-color image enhancement algorithm is described. During the first phase noises are removed from the picture. Wavelet transformation has been chosen to perform it, because it allows easily remove high-frequency parts. Also, noise components, especially big random surges of signal, could be presented like set of local features of signals. Noise can be reduced by thresholding this features. During the second phase brightness and contrast are automatically tuned up using genetic algorithm. Genetic algorithms, which are effective methods of multidimensional optimization, allow quick selection of optimal values of transformation parameters, using objective optimization criterion.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
Pages846-851
Number of pages6
Volume2
Publication statusPublished - 2009
Event2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 - Las Vegas, NV, United States
Duration: 13 Jul 200916 Jul 2009

Other

Other2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
CountryUnited States
CityLas Vegas, NV
Period13.7.0916.7.09

Fingerprint

Image enhancement
Wavelet transforms
Image processing
Genetic algorithms
Phase noise
Luminance
Color
Defects

Keywords

  • Genetic algorithms
  • Image enhancement
  • Wavelet transform

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Cite this

Belousov, A. A., Spitsyn, V. G., & Sidorov, D. V. (2009). Application of wavelet transform and genetic algorithms for image processing. In Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 (Vol. 2, pp. 846-851)

Application of wavelet transform and genetic algorithms for image processing. / Belousov, Artem A.; Spitsyn, Vladimir G.; Sidorov, Dmitry V.

Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. Vol. 2 2009. p. 846-851.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Belousov, AA, Spitsyn, VG & Sidorov, DV 2009, Application of wavelet transform and genetic algorithms for image processing. in Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. vol. 2, pp. 846-851, 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, Las Vegas, NV, United States, 13.7.09.
Belousov AA, Spitsyn VG, Sidorov DV. Application of wavelet transform and genetic algorithms for image processing. In Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. Vol. 2. 2009. p. 846-851
Belousov, Artem A. ; Spitsyn, Vladimir G. ; Sidorov, Dmitry V. / Application of wavelet transform and genetic algorithms for image processing. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009. Vol. 2 2009. pp. 846-851
@inproceedings{a760c44a0c564008af4b6f286dec62e3,
title = "Application of wavelet transform and genetic algorithms for image processing",
abstract = "Getting clear high detailed images with high contrast is an important task in many spheres of science and engineering. However, it's not always possible because of imperfection of devices or environment conditions. This all leaded to development of different methods of image enhancement. In this article a developed two-phase full-color image enhancement algorithm is described. During the first phase noises are removed from the picture. Wavelet transformation has been chosen to perform it, because it allows easily remove high-frequency parts. Also, noise components, especially big random surges of signal, could be presented like set of local features of signals. Noise can be reduced by thresholding this features. During the second phase brightness and contrast are automatically tuned up using genetic algorithm. Genetic algorithms, which are effective methods of multidimensional optimization, allow quick selection of optimal values of transformation parameters, using objective optimization criterion.",
keywords = "Genetic algorithms, Image enhancement, Wavelet transform",
author = "Belousov, {Artem A.} and Spitsyn, {Vladimir G.} and Sidorov, {Dmitry V.}",
year = "2009",
language = "English",
isbn = "9781601321190",
volume = "2",
pages = "846--851",
booktitle = "Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009",

}

TY - GEN

T1 - Application of wavelet transform and genetic algorithms for image processing

AU - Belousov, Artem A.

AU - Spitsyn, Vladimir G.

AU - Sidorov, Dmitry V.

PY - 2009

Y1 - 2009

N2 - Getting clear high detailed images with high contrast is an important task in many spheres of science and engineering. However, it's not always possible because of imperfection of devices or environment conditions. This all leaded to development of different methods of image enhancement. In this article a developed two-phase full-color image enhancement algorithm is described. During the first phase noises are removed from the picture. Wavelet transformation has been chosen to perform it, because it allows easily remove high-frequency parts. Also, noise components, especially big random surges of signal, could be presented like set of local features of signals. Noise can be reduced by thresholding this features. During the second phase brightness and contrast are automatically tuned up using genetic algorithm. Genetic algorithms, which are effective methods of multidimensional optimization, allow quick selection of optimal values of transformation parameters, using objective optimization criterion.

AB - Getting clear high detailed images with high contrast is an important task in many spheres of science and engineering. However, it's not always possible because of imperfection of devices or environment conditions. This all leaded to development of different methods of image enhancement. In this article a developed two-phase full-color image enhancement algorithm is described. During the first phase noises are removed from the picture. Wavelet transformation has been chosen to perform it, because it allows easily remove high-frequency parts. Also, noise components, especially big random surges of signal, could be presented like set of local features of signals. Noise can be reduced by thresholding this features. During the second phase brightness and contrast are automatically tuned up using genetic algorithm. Genetic algorithms, which are effective methods of multidimensional optimization, allow quick selection of optimal values of transformation parameters, using objective optimization criterion.

KW - Genetic algorithms

KW - Image enhancement

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=84864955476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864955476&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781601321190

VL - 2

SP - 846

EP - 851

BT - Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009

ER -