Image contrast improvement method using genetic algorithm

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详细

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.

作者简介

V. Gridin

Design Information Technologies Center Russian Academy of Sciences

编辑信件的主要联系方式.
Email: info@ditc.ras.ru

Doctor of Technical Sciences, Professor, Scientific Director

俄罗斯联邦, Odintsovo, Moscow Region

K. Domanov

Design Information Technologies Center Russian Academy of Sciences

Email: domanovki@student.bmstu.ru

Research Engineer

俄罗斯联邦, Odintsovo, Moscow Region

V. Solodovnikov

Design Information Technologies Center Russian Academy of Sciences

Email: info@ditc.ras.ru

Ph.D., Director

俄罗斯联邦, Odintsovo, Moscow Region

参考

  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.

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