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Image Enhancement Techniques: An Exhaustive Review

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Intelligent Computing Applications for Sustainable Real-World Systems (ICSISCET 2019)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 13))

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Abstract

The various image enhancement techniques are used as a preprocessing step in the image processing task to make the image more productive to be processed in the computer. In this work, the brief study of the various image enhancement methods like Histogram Equalization, Adaptive Histogram Equalization, Contrast Limited Adaptive Histogram Equalization is done. These techniques are verified based on MATLAB an image processing toolbox along with their corresponding images and histograms. Different techniques have their own pros and cons so here the juxtaposition of the various methods have been done along with the calculation of the different image parameters like - Standard Deviation, Mean, Mode, Medians, Mean Squared Error for these enhancement techniques.

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Author information

Authors and Affiliations

  1. Department of CSE and IT, Madhav Institute of Technology and Science, Gwalior, India

    Shradha Dubey & Manish Dixit

Authors
  1. Shradha Dubey
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  2. Manish Dixit
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Corresponding author

Correspondence to Shradha Dubey .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, India

    Manjaree Pandit

  2. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, India

    Laxmi Srivastava

  3. Mechanical Engineering Dept, A-2 Quarter, S.V. National Institute of Technology, Surat, Gujarat, India

    Ravipudi Venkata Rao

  4. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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Dubey, S., Dixit, M. (2020). Image Enhancement Techniques: An Exhaustive Review. In: Pandit, M., Srivastava, L., Venkata Rao, R., Bansal, J. (eds) Intelligent Computing Applications for Sustainable Real-World Systems. ICSISCET 2019. Proceedings in Adaptation, Learning and Optimization, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-44758-8_34

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