Survey of Contrast Enhancement Techniques based on Histogram Equalization

Kaur, Manpreet; Kaur, Jasdeep; Kaur, Jaspreet

doi:10.14569/ijacsa.2011.020721

Cited by 152 publications

(97 citation statements)

References 23 publications

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“…It gives unlikely effects in images; nevertheless it is very helpful for images [1].Histogram Equalization (HE) is process which is put on improve the contrast of an image. In that it's possibly not required that the contrast of a graphic will be improved [2].…”

Section: Different Techniques Of Image Enhancement 21 Histogram Equamentioning

confidence: 99%

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Study on Various Techniques of Image Enhancement

Kaur¹,

Kumar²

2017

IJCA

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This paper has discuss the various techniques for image enhancement i.e histogram equalization, Brightness preserving bi-histogram equalization(BBHE), Dualistic SubImage Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-HE Method (MMBEBHE), Recursive Mean -Separate HE Method (RMSHE),Mean brightness preserving histogram equalization(MBPHE).As well as it represents the comparison between the various techniques that shows the image enhances the overall contrast and visibility of local details. The review has shown that contrast enhancement approach based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. General TermsEnhance the contrast of images,comparison of different techniques of image enhancement.

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Section: Different Techniques Of Image Enhancement 21 Histogram Equamentioning

confidence: 99%

“…Image enhancement contains number of procedures are used to improve the visible act of an imag [1]e. The main intent behind image improvement is to improve features of an image [2].…”

Section: Introductionmentioning

confidence: 99%

Study on Various Techniques of Image Enhancement

Kaur¹,

Kumar²

2017

IJCA

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“…It improves contrast by obtaining a uniform histogram. Kaur et al [22] presented a survey of contrast enhancement techniques based on histogram equalization.…”

Section: Contrast Enhancementmentioning

confidence: 99%

Brain Tumor Analysis of Rician Noise Affected MRI Images

Renukalatha¹,

Kalyanasundaram²

2016

IJCA

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Magnetic Resonance Imaging (MRI) established itself as a key imaging modality in diagnosis and treatment of brain tumors. Automatic segmentation of tumors becomes a tedious task due to complex anatomical brain structure. In addition, presence of noise degrades the quality of MRI scans. MRI images are usually corrupted by Rician noise which would mislead the image analysis algorithms and results in improper diagnosis of the diseases. Also, poor tumor boundary becomes a major hurdle for the subsequent stages of tumor analysis such as: feature extraction, classification and quantification. Classification accuracy mainly depends on quality of the denoised images and sharpness of the tumor boundary. This paper investigates the performance evaluation of different image matting techniques to extract tumor from Rician noise affected MRI brain images. General Terms Medical Image ProcessingKeywords MRI Brain tumor, Rician noise, Region of interest (ROI), image matting and Sensitivity.

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“…The synthetic image is corrected by applying the reciprocal of the terms employed to generate it, as follows, (4) where the matrix of thicknesses T(x,y), which incorporates the object and its defects, is replaced by T 0 (x,y), which does contain information only about the pure object. Therefore, as the defects will not be corrected but solely the main object, it would vanish as if it were transparent to the interrogating radiation, while the opaque defects would arise against a homogeneous background.…”

Section: µ =mentioning

confidence: 99%

“…Some techniques have been developed [2][3][4][5][6][7] to improve the contrast by histogram equalization, which basically stretch the dynamic range and flatten the image histogram, distributing thus the pixel intensities of a digital image more homogeneously within the ranges 0-255 or 0-65,535 for jpg or tiff images respectively. These techniques employ a blind approach, for no information is provided about the geometric or physical properties of the object being inspected, or about its geometric arrangement with regard to source and detector.…”

Section: Introductionmentioning

confidence: 99%

An algorithm to unveil the inner structure of objects concealed by beam divergence in radiographic image acquisition systems

Almeida¹,

Silvani

Lopes

2014

AIP Conference Proceedings

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Abstract. Two main parameters rule the performance of an Image Acquisition System, namely, spatial resolution and contrast. For radiographic systems using cone beam arrangements, the farther the source, the better the resolution, but the contrast would diminish due to the lower statistics. A closer source would yield a higher contrast but it would no longer reproduce the attenuation map of the object, as the incoming beam flux would be reduced by unequal large divergences and attenuation factors. This work proposes a procedure to correct these effects when the object is comprised of a hullor encased in it -possessing a shape capable to be described in analytical geometry terms. Such a description allows the construction of a matrix containing the attenuation factors undergone by the beam from the source until its final destination at each coordinate on the 2D detector. Each matrix element incorporates the attenuation suffered by the beam after its travel through the hull wall, as well as its reduction due to the square of distance to the source and the angle it hits the detector surface. When the pixel intensities of the original image are corrected by these factors, the image contrast, reduced by the overall attenuation in the exposure phase, are recovered, allowing one to see details otherwise concealed due to the low contrast. In order to verify the soundness of this approach, synthetic images of objects of different shapes, such as plates and tubes, incorporating defects and statistical fluctuation, have been generated, recorded for further comparison and afterwards processed to improve their contrast. The developed algorithm which, generates processes and plots the images has been written in Fortran 90 language. As the resulting final images exhibit the expected improvements, it therefore seemed worthwhile to carry out further tests with actual experimental radiographies.

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Survey of Contrast Enhancement Techniques based on Histogram Equalization

Abstract: Abstract-ThisKeywords-component image processing; contrast enhancement; histogram equalization; minimum mean brightness error; brightness preserving enhancement, histogram partition.

Cited by 152 publications

References 23 publications

Study on Various Techniques of Image Enhancement

Study on Various Techniques of Image Enhancement

Brain Tumor Analysis of Rician Noise Affected MRI Images

An algorithm to unveil the inner structure of objects concealed by beam divergence in radiographic image acquisition systems

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