An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping

Zhu, Yun; Huang, Cheng

doi:10.1016/j.phpro.2012.03.132

Cited by 84 publications

(45 citation statements)

References 4 publications

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“…Adaptive histogram based Algorithm (AHEA) [13] Introduces a β parameter in the gray level mapping formula. It avoids excessive gray levels and bright regions.…”

Section: High Computational Cost Involved With Bpdhementioning

confidence: 99%

“…But few of the methods like DRSHE [15], AGCWD [20] still lacks in reducing block noise and partially enhance fine details of the image resulting in high computational cost. Also few techniques like AHEA [13] are only applicable to CT image processing in medicine. Later various techniques evolved with the concept of histogram clipping as described in table 2.…”

Section: Comparisons Between the Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Supriya¹,

Subaji²

2017

IJSIP

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“…Adaptive histogram based Algorithm (AHEA) [13] Introduces a β parameter in the gray level mapping formula. It avoids excessive gray levels and bright regions.…”

Section: High Computational Cost Involved With Bpdhementioning

confidence: 99%

Section: Comparisons Between the Techniquesmentioning

confidence: 99%

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Supriya¹,

Subaji²

2017

IJSIP

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show abstract

“…In this work, an adaptive histogram-equalization technique is employed to emphasize local contrast, such that the image is considered in smaller domains of size n× m, and histograms are generated locally in these domains. 14 In the examples seen in Figure 3, we have set n=m=20.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Computational advances applied to medical image processing: an update

João¹,

Gambaruto²,

Tiago³

et al. 2016

OAB

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“…Modified Histogram Equalization (MHE) alters the accumulations in the input histogram before histogram equalization is applied in order to enhance the contrast of the image, while preserving the discrete structures and fine details of the image [12]. Adaptive Histogram Equalization Algorithm (AHEA) uses information entropy as the target function and introduces a new parameter in the histogram equalization formula to adaptively adjust the spacing of two adjacent gray levels in the output histogram based on the type of input image [13]. [5].…”

Section: Related Workmentioning

confidence: 99%

“…(13), OutputImage InputImage SD SD SD = − (13) In addition to the output-input standard deviation, the deviation of gray levels in the image can be used as a contrast improvement investigator. This deviation is known as contrast improvement evaluation, C. It can be computed and subsequently converted into decibels (dB) using the image contrast function as shown in Eq.…”

Section: Quantitative Analysismentioning

confidence: 99%

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

Tang

Isa

2014

J. ICT.Res.Appl.

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Abstract. This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle-or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.

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An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping

Cited by 84 publications

References 4 publications

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Computational advances applied to medical image processing: an update

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

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