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DE-IE: differential evolution for color image enhancement

Author

Listed:
  • Sushil Kumar

    (Indian Institute of Technology Roorkee)

  • Millie Pant

    (Indian Institute of Technology Roorkee)

  • Amiya Kumar Ray

    (Indian Institute of Technology Roorkee)

Abstract

Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed.

Suggested Citation

  • Sushil Kumar & Millie Pant & Amiya Kumar Ray, 2018. "DE-IE: differential evolution for color image enhancement," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(3), pages 577-588, June.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:3:d:10.1007_s13198-014-0278-6
    DOI: 10.1007/s13198-014-0278-6
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    References listed on IDEAS

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    1. Coelho, Leandro dos Santos & Sauer, João Guilherme & Rudek, Marcelo, 2009. "Differential evolution optimization combined with chaotic sequences for image contrast enhancement," Chaos, Solitons & Fractals, Elsevier, vol. 42(1), pages 522-529.
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    Cited by:

    1. Katyayani Kashyap & Tarun K. Sharma & Jitendra Rajpurohit, 2020. "Logistic map and wavelet transform based differential evolution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 506-514, April.

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