Histogram Equalization using Neighborhood Metrics

Eramian, Mark; Mould, David

doi:10.1109/crv.2005.47

Cited by 45 publications

(29 citation statements)

References 12 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%

“…Face recognition accuracy improves as results of enhancing the facial regions affected by blur and shadow. Neighbourhood metrics in use are Voting metric [6,12,13,14], Distinction metric [6,12,13,14], fuzzy approach on the neighbourhood pixels [14].…”

Section: Neighborhood Metrics (Nm)mentioning

confidence: 99%

“…Global Histogram Equalization stretches the dynamic range of the image histogram using the following transformation function [12,13]:…”

Section: Global Histogram Equalization (Ghe)mentioning

confidence: 99%

“…Completely overlapped uniformly decrementing histogram equalization (COUDSHE) framework reduces the computational complexity of LHE as in POSHE but overcomes the drawbacks of POSHE which suffers from equalizing the symmetrical image regions [11]. The principle behind the introduction of neighbourhood metrics with histogram equalization is to reduce the gaps between bins and to increase the spread of histograms [12,13]. Image contrast is an important feature of image enhancement [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…The principle behind the introduction of neighbourhood metrics with histogram equalization is to reduce the gaps between bins and to increase the spread of histograms [12,13]. Image contrast is an important feature of image enhancement [12,13]. Distinction Metric, Voting Metric, Average metric contrast difference metric were proposed to decompose the large bins into sub bins which are then equalized independently [13].…”

Section: Introductionmentioning

confidence: 99%

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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: Neighborhood Metrics (Nm)mentioning

confidence: 99%

“…Global Histogram Equalization stretches the dynamic range of the image histogram using the following transformation function [12,13]:…”

Section: Global Histogram Equalization (Ghe)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ganesan¹,

Mohammed²

2017

IJIES

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Automated compensation of light attenuation in confocal microscopy by exact histogram specification

Stanciu

Coltuc

2009

Microscopy Res & Technique

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Confocal laser scanning microscopy (CLSM) enables us to capture images representing optical sections on the volume of a specimen. The images acquired from different layers have a different contrast: the images obtained from the deeper layers of the specimen will have a lower contrast with respect to the images obtained from the topmost layers. The main reasons responsible for the effects described above are light absorption and scattering by the atoms and molecules contained in the volume through which the light passes. Also light attenuation can be caused by the inclination of the observed surface. In the case of the surfaces that have a steep inclination, the reflected light will have a different direction than the one of the detector. We propose a technique of digital image processing that can be used to compensate the effects of light attenuation based on histogram operations. We process the image series obtained by CLSM by exact histogram specification and equalization. In this case, a strict ordering among pixels must be induced in order to achieve the exact histogram modeling. The processed images will end up having exactly the specified histogram and not a histogram with a shape that just resembles to the specified one, as in the case of classical histogram specification algorithms. Experimental results and theoretical aspects of the induced ordering are discussed, as well as a comparison between several histogram modeling techniques with respect to the processing of image series obtained by confocal microscopy.

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