DATA PROCESSING AND ANALYSIS
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
V. N. Gridin, K. I. Domanov, V. I. Solodovnikov Image Contrast Improvement Method Using Genetic Algorithm
MATHEMATICAL MODELING
MANAGEMENT AND DECISION MAKING
MATHEMATICAL FOUNDATIONS OF INFORMATION TECHNOLOGY
V. N. Gridin, K. I. Domanov, V. I. Solodovnikov Image Contrast Improvement Method Using Genetic Algorithm
Abstract. 

The paper presents a method for local image contrast enhancement based on the distribution of gray levels in the vicinity of each individual pixel. The considered approach was automated using a genetic algorithm, which made it possible to eliminate the need for manual adjustment of the transformation parameters. The necessary criteria for assessing the quality of images are selected, among which the main ones are: the number of edge pixels, their total intensity, the measure of image entropy and the measure of brightness adaptation. Software components have been implemented and their functioning has been tested on various classes of images, which has shown the success of this approach for images with a high density of distribution of gradations of brightness, uniform illumination and a weak gradient of boundary pixels.

Keywords: 

image, preprocessing, brightness, contrast, quality, pixel, neighborhood, genetic algorithm, quality assessment criteria.

PP. 67-75.

DOI 10.14357/20718632230207
 
References

1. Gonzales R., Woods R. Digital Image Processing. 3rd edition, corrected and enlarged. Moscow, Technosphere, 2012, p. 1104.
2. Batishchev D.I., Neimark E.A., Starostin N.V. Application of genetic algorithms to solving discrete optimization problems. Educational and methodological material for the advanced training program "Information Technology and Computer Modeling in Applied Mathematics". Nizhny Novgorod, 2007, p. 85.
3. Munteanu C., Rosa A. Gray-Scale Image Enhancement as an Automatic Process Driven by Evolution. IEEE   Transactions on Systems, Man, and Cybernetics, 2004, p.   1292–1298
4. Panchenko T.V. Genetic algorithms: teaching aid. Astrakhan,  Astrakhan University, 2007, p. 6.
5. Voronovsky G.K. Genetic algorithms, artificial neural      networks and problems of virtual reality. Kharkov,  OSNOVA, 1997, p. 112.
6. Tim Jones M. Programming artificial intelligence in applications: per. from English. DMK Press, 2004, p. 312.
7. Demin A.Yu., Dorofeev V.A. Parallelization of the algorithm for selecting the boundaries of objects based on the structural-graphical representation. Tomsk Polytechnic   University, 2013, p. 160.
8. Martyanova A.V., Labunets V.G. The task of aggregation when highlighting the boundaries of objects in the image. Bulletin of SUSU, 2015, p. 6.
9. Tsvetkov O.V., Polivanaite L.V., Kutsenko S.A., Repina  M.V. A simple, highly informative metric for evaluating image quality in biomedical systems. St.Petersburg,
Biotechnosphere, 2014, p.56.
10. PSNR and SSIM or how to work with images under C - URL: https://habr.com/ru/post/126848/ - Habr (accessed 09/09/2022).
11. Akhilesh Verma. A Survey on Image Contrast Enhancement   Using Genetic Algorithm. International Journal  of Scientific and Research Publications, Volume 2, Issue 7, 2012.
 

2024 / 01
2023 / 04
2023 / 03
2023 / 02

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".