Histogram Equalization Techniques for Contrast Enhancement: A Review

Das, Sourav; Gulati, Tarun; Mittal, Vikas

doi:10.5120/20017-2027

Cited by 8 publications

(8 citation statements)

References 20 publications

(12 reference statements)

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“…However utilization of median values shares the same number of pixels in each partition. Thereafter it divides the histogram into 2 r pieces of sub-histograms and it will preserve the brightness to maximum extent rather than other partitioning methods where r is recursion level and its value is defined by the user [25].…”

Section: 5recursive Sub_image Histogram Equalization (Rsihe)mentioning

confidence: 99%

Contrast Enhancement in Gray Level Images

Sultan¹,

Yonis²

2019

J.Edu.Sci.

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Contrast enhancement is a very important step in image processing, pattern recognition and computer vision .Histogram Equalization (HE) is widely used for contrast Enhancement. This paper discussed five techniques of contrast enhancement, i.e. Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness preserving Bi_Histogram Equalization (BBHE), Dualistic Sub_Image Histogram Equalization (DSIHE) and Recursive Sub_Image Histogram equalization (RSIHE). Also it presented the comparison among the various techniques that show the image enhances the overall contrast and visibility of local details, by using two measures: Peak-Signal-to-Noise-Ratio (PSNR) and Absolute Mean Brightness Error (AMBE) to check the signal and brightness power respectively. This work is programmed by MATLAB VER.7.8.

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Section: 5recursive Sub_image Histogram Equalization (Rsihe)mentioning

confidence: 99%

Contrast Enhancement in Gray Level Images

Sultan¹,

Yonis²

2019

J.Edu.Sci.

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“…2 (e). Since the dynamic range is compressed, we use histogram equalization (HE) [3] to stretch the dynamic range, the result is shown in Fig. 2 (f).…”

Section: Reduce Influence Caused By Asymmetric Illumination Using mentioning

confidence: 99%

A single scale retinex based method for palm vein extraction

Wang

Peng

et al. 2016

2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference

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Palm vein recognition is a novel biometric identification technology. But how to gain a better vein extraction result from the raw palm image is still a challenging problem, especially when the raw data collection has the problem of asymmetric illumination. This paper proposes a method based on single scale Retinex algorithm to extract palm vein image when strong shadow presents due to asymmetric illumination and uneven geometry of the palm. We test our method on a multispectral palm image. The experimental result shows that the proposed method is robust to the influence of illumination angle and shadow. Compared to the traditional extraction methods, the proposed method can obtain palm vein lines with better visualization performance (the contrast ratio increases by 18.4%, entropy increases by 1.07%, and definition increases by 18.8%).Comment: 4 pages, 4 figures, received by 2016 IEEE Information Technology,Networking,Electronic and Automation Control Conference(ITNEC 2016

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“…Currently, three types of methods have been developed to solve this problem [2]: preprocessing-based methods, lighting model-based techniques, and lighting normalization-based algorithms. The first method processes image by histogram equalization [3][4][5], gamma correction [6], or homomorphic filtering [7], etc. Clearly, shadow occlusion problems cannot be solved well.…”

Section: Introductionmentioning

confidence: 99%

Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

2021

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A hierarchic clustering-based enhancement is proposed to solve the luminance compensation of face recognition in the dark field. First, the face image is divided into five levels by a clustering method. Second, the results above are mapped into three hierarchies according to the histogram thresholds. A low, a middle, and a high-intensity block are found. Third, two kinds of linear transforms are performed to the high and the low-intensity blocks. Finally, a center wrap function-based enhancement is carried out. Experiment results show our method can improve both the face recognition accuracy and image quality.

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Histogram Equalization Techniques for Contrast Enhancement: A Review

Cited by 8 publications

References 20 publications

Contrast Enhancement in Gray Level Images

Contrast Enhancement in Gray Level Images

A single scale retinex based method for palm vein extraction

Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

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