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Adaptive tri-plateau limit tri-histogram equalization algorithm for digital image enhancement

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Abstract

Histogram equalization (HE) is one of the most important techniques for contrast enhancement of digital images. Conventional HE methods persuade excessive enhancement, unnatural artifacts and brightness transform resulting abnormal and unusual appearance. To solve such problems, a novel tri-plateau limit-oriented tri-histogram equalization technique is suggested for digital image enhancement, where histogram of the input image is initially separated in three sub-histograms using separation threshold parameters. Next, plateau limit criteria for sub-histograms are formulated using the average of the mean and the median of each sub-histogram, and subsequently, a redistributed parameter is calculated and merged with each sub-histogram to restrict over-enhancement. Finally, modified sub-histograms are equalized separately and the enhanced image is produced by incorporating the images accomplished by the transformation function. Experimental results demonstrate that the proposed technique efficiently enhances the contrast of images, while visual quality assessments and quantitative measures, like average information content (AIC), feature similarity index measure (FSIM), multi scale structural similarity index measure (MS-SSIM), visual saliency-induced index (VSI), and gradient magnitude similarity deviation (GMSD) effectively validate the superiority of the proposed algorithm with respect to the other traditional state-of-the-art HE methods.

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  1. Department of Computer Science and Engineering, National Institute of Technology, Agartala, India

    Abhisek Paul

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Paul, A. Adaptive tri-plateau limit tri-histogram equalization algorithm for digital image enhancement. Vis Comput 39, 297–318 (2023). https://doi.org/10.1007/s00371-021-02330-z

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