Image contrast enhancement using bi-histogram equalization with neighborhood metrics

Sengee, Nyamlkhagva; Sengee, Altansukh; Choi, Heung‐Kook

doi:10.1109/tce.2010.5681162

Cited by 80 publications

(49 citation statements)

References 9 publications

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“…The ideal greyscale image histogram is perfectly flat and makes use of every available grey value in the image format [6,12]. Application of GHE on illumination affected images, it is found that histograms do not use the entire range of gray scale value and the histograms are not flat [13].…”

Section: Neighborhood Metrics (Nm)mentioning

confidence: 99%

“…The concept behind gradient and edge extraction [3]. Histogram equalization is the most prominently used contrast enhancement technique due to its efficient performance on almost all type of images [6,7,8]. Histogram equalization is simple technique, which distributes pixel values uniformly to obtain a high contrast image [8].…”

Section: Introductionmentioning

confidence: 99%

“…GHE is a holistic approach, which uses the full input image to obtain the gray level transformation function [6]. This results in a overall contrast improvement but GHE does not acclimatize to the local brightness.…”

Section: Introductionmentioning

confidence: 99%

“…LHE overcomes the drawbacks of GHE. LHE uses a sliding window method to remap the intensities of each centre pixel in the window using the gray level transformation function obtained from the neighbourhood pixels [6,10]. Despite being computationally, high LHE results in over enhanced image [6,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…LHE uses a sliding window method to remap the intensities of each centre pixel in the window using the gray level transformation function obtained from the neighbourhood pixels [6,10]. Despite being computationally, high LHE results in over enhanced image [6,10,11]. To reduce the computation complexity and obtain the advantages of local adaptability Partially Overlapped Sub-Block Histogram Equalization (POSHE) was proposed [10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy

Ganesan¹,

Mohammed²

2017

IJIES

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Automatic Face recognition system struggles to recognize face images acquired at varying illumination conditions, facial expression, aging, and pose. The focus of this research work is to enhance the illumination affected face images, which subsequently results in improved face recognition accuracy. This paper presents a new contrast enhancement technique for face images. It is a hybrid contrast enhancement technique based on the combinatorial approach of Completely Overlapped Uniformly Decrementing Sub-block Histogram Equalization (COUDSHE) with various neighbourhood metric. COUDSHE concentrated to bring out a unique framework for localizing a global technique to adapt efficiently to local lightening conditions. The idea of using neighbourhood metric with histogram equalization results in enhancing the contrast of the image. Hence, the combination of COUDSHE with neighbourhood metrics results in enhanced images that are both adaptive to local brightness and good contrast enhancement. The application of our hybrid technique on the Extended Yale B Database has proved to improve the contrast of images and as a result, there is significant improvement in the recognition accuracy of the face recognition system implemented using Principal Component Analysis.

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Section: Neighborhood Metrics (Nm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy

Ganesan¹,

Mohammed²

2017

IJIES

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Contrast enhancement dynamic histogram equalization for medical image processing application

Ismail

Sim

2011

Int J Imaging Syst Tech

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Image processing requires an excellent image contrastenhancement technique to extract useful information invisible to the human or machine vision. Because of the histogram flattening, the widely used conventional histogram equalization image-enhancing technique suffers from severe brightness changes, rendering it undesirable. Hence, we introduce a contrast-enhancement dynamic histogram-equalization algorithm method that generates better output image by preserving the input mean brightness without introducing the unfavorable side effects of checkerboard effect, artefacts, and washed-out appearance. The first procedure of this technique is; normalizing input histogram and followed by smoothing process. Then, the break point detection process is done to divide the histogram into subhistograms before we can remap the gray level allocation. Lastly, the transformation function of each subhistogram is constructed independently. V

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A Quantitative Analysis of Histogram Equalization-Based Methods on Fundus Images for Diabetic Retinopathy Detection

Suma¹,

Kumar²

2018

Computational Intelligence and Big Data Analytics

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Image contrast enhancement using bi-histogram equalization with neighborhood metrics

Cited by 80 publications

References 9 publications

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy

Contrast enhancement dynamic histogram equalization for medical image processing application

A Quantitative Analysis of Histogram Equalization-Based Methods on Fundus Images for Diabetic Retinopathy Detection

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