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A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

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Abstract

In the field of image processing, there are several problems where the efficient search has to be performed in complex search domain to find an optimal solution. Image enhancement which improves the quality of an image for visual analysis and/or machine understanding is one of these problems. There is no unique image enhancement technique and it’s measurement criterion which satisfies all the necessity and quantitatively judge the quality of a given image respectively. Thus sometimes proper image enhancement problem becomes hard and takes large computational time. In order to overcome that problem, researchers formulated the image enhancement as optimization problems and solved using Nature-Inspired Optimization Algorithms (NIOAs) which starts a new era in image enhancement field. This study presents an up-to-date review over the application of NIOAs in image enhancement domain. The key issues which are involved in the formulation of NIOAs based image enhancement models are also discussed here.

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Funding

This study was funded under DST-PURSE scheme.

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Authors and Affiliations

  1. Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India

    Krishna Gopal Dhal

  2. Skybound Digital LLC, Kolkata, West Bengal, India

    Swarnajit Ray

  3. Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, India

    Arunita Das

  4. Department of Engineering and Technological Studies, University of Kalyani, Kalyani, Nadia, India

    Sanjoy Das

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  1. Krishna Gopal Dhal
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Correspondence to Krishna Gopal Dhal.

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Conflict of interest

Krishna Gopal Dhal has received research Grants from PURSE Scheme, DST, India. Swarnaji Ray, Arunita Das, Sanjoy Das declare that thay have no conflict of interest.

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Dhal, K.G., Ray, S., Das, A. et al. A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain. Arch Computat Methods Eng 26, 1607–1638 (2019). https://doi.org/10.1007/s11831-018-9289-9

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