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Parameter Selection of Contrast Limited Adaptive Histogram Equalization Using Multi-Objective Flower Pollination Algorithm

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 436)

Abstract

Contrast enhancement is one of the major fields of image processing. It is a technique that is employed in devices in order to enhance the visual quality. Contrast enhancement usually deals with low contrast and bad illumination of the scenery. There are many different methods, which are devised to overcome the problems of enhancing image quality, exist in the literature. Histogram equalization (HE) is one of these techniques. To handle the drawbacks of HE, a local contrast limited adaptive histogram equalization (CLAHE) method, which is a variant of adaptive histogram equalization (AHE), is exploited. CLAHE differs from AHE in the sense that it accepts two parameters, namely, number of tiles (NT) and clip limit (CL). Good selection of CLAHE parameters is significant for the images gained in the end. In this study, multi-objective flower pollination algorithm (MOFPA) enhanced CLAHE (MOFPAE-CLAHE) is proposed to select the most appropriate parameters for CLAHE. MOFPA evaluates the fitness of the resulting images by employing a multi-objective fitness function which uses entropy and fast noise variance estimation (FNVE). For evaluation, 3 different datasets are used. The results are compared with the other state-of-the-art methods using entropy, absolute mean brightness error (AMBE), peak signal-to-noise ratio (PSNR), structural similarity index (SSI) and computational time (CT). The experimental results show that the proposed MOFPAE-CLAHE method could be used for contrast enhancement, since it outperforms most of the cutting-edge algorithms.

Keywords

  • Contrast enhancement
  • Histogram equalization
  • Parameter optimization

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Kuran, U., Kuran, E.C., Er, M.B. (2022). Parameter Selection of Contrast Limited Adaptive Histogram Equalization Using Multi-Objective Flower Pollination Algorithm. In: Seyman, M.N. (eds) Electrical and Computer Engineering. ICECENG 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-031-01984-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-01984-5_9

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