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Image contrast enhancement using geometric mean filter

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Abstract

Histogram equalization HE is one of the most popular methods for image contrast enhancement. However, the intensity of the input image plays an important role on its performance. In particular, HE fails to enhance images with a dominant color. Therefore, several techniques were proposed to tackle this problem. Some are built for brightness preservation, and others aim to maximize the preservation of structural information. In this paper, we propose an efficient HE enhancement technique that is not only addresses brightness preservation but also both edge and structural information preservation. The proposed technique investigates the geometric mean filter for smoothing the peaks in the histogram before applying the HE. To support our claims, a set of experiments were conducted. Remarkably, through qualitative and quantitative evaluations, results demonstrate that the performance of the proposed method, when compared with a set of other state-of-the-art methods including HE, CLAHE, Log-Power, BPDFHE and DWT–SVD, shows a significant improvement especially in terms of structural and edge preservation.

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Notes

  1. In the paper, the terms histogram peaks, spikes and outliers were used interchangeably to refer to the histogram of images with a dominant color.

  2. The golden ratio or the golden mean equals \((1+\sqrt{5})/2\), this number satisfies the condition \((a+b)/a = a/b = \text {golden ratio}\), where a and b are two positive numbers and \(a>b\) [16].

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Authors and Affiliations

  1. The University of Jordan, Amman, Jordan

    Hazem Hiary, Rawan Zaghloul, Aryaf Al-Adwan & Moh’d B. Al-Zoubi

Authors
  1. Hazem Hiary
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  2. Rawan Zaghloul
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  3. Aryaf Al-Adwan
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  4. Moh’d B. Al-Zoubi
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Correspondence to Hazem Hiary.

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Hiary, H., Zaghloul, R., Al-Adwan, A. et al. Image contrast enhancement using geometric mean filter. SIViP 11, 833–840 (2017). https://doi.org/10.1007/s11760-016-1029-8

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