Thresholded and Optimized Histogram Equalization for contrast enhancement of images

Shanmugavadivu, P.; Balasubramanian, K.

doi:10.1016/j.compeleceng.2013.06.013

Cited by 33 publications

(17 citation statements)

References 13 publications

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“…Related methods were proposed in [3]- [6] and [10]- [17] to preserve the mean brightness and reduce the visual artifacts as much as possible in order to enhance the image contrast. In the case where  = 1, TF is the same as HE.…”

Section: Resultsmentioning

confidence: 99%

“…Shanmugavadivu and Balasubramanian proposed the use of thresholded and optimized histogram equalization to consider brightness preservation and contrast enhancement simultaneously [17]. To classify an object and background region in an image, a histogram of the image is divided using Otsu's method and the divided sub-histograms are then clamped independently.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Contrast Enhancement by Transfer Function Modification

Bae

Ahn

Altunbasak

2017

ETRI J

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In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram‐based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Contrast Enhancement by Transfer Function Modification

Bae

Ahn

Altunbasak

2017

ETRI J

View full text Add to dashboard Cite

show abstract

“…In PSO, discrete entropy is selected as the objective function for finding the best intensity values of pixels to transfer maximum visual perception. The optimal values of u , v , α , and β are found using PSO algorithm . The values of lower threshold b and d are less important in controlling the enhancement and are set at a very low value as 0.0001.…”

Section: Methodsmentioning

confidence: 99%

“…The optimal values of u, v, α, and β are found using PSO algorithm. 23 The values of lower threshold b and d are less important in controlling the enhancement and are set at a very low value as 0.0001. Next, the respective cumulative density functions for f L and f U are defined by using Equations (14) and (15).…”

Section: Histogram Splitting and Equalizationmentioning

confidence: 99%

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Veluchamy

Mayathevar

Subramani

2019

Int J Imaging Syst Tech

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Medical image segmentation is crucial for neuroscience research and computer‐aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast‐enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub‐histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub‐histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.

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“…It combines spatial edge information with gray information to enhance the local details (Leng, ). Shanmugavadivu et al () proposed thresholded and optimized histogram equalization for contrast enhancement of images in which the input image is segmented based on Otsu's principle. The probability density function of image histogram is modified using weighing constraints.…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images

Babu

Rajamani

2015

Int J Imaging Syst Tech

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Histogram equalization is a well-known technique used for contrast enhancement. The global HE usually results in excessive contrast enhancement because of lack of control on the level of contrast enhancement. A new technique named modified histogram equalization using real coded genetic algorithm (MHERCGA) is aimed to sweep over this drawback. The primary aim of this paper is to obtain an enhanced method which keeps the original brightness. This method incorporates a provision to have a control over the level of contrast enhancement and applicable for all types of image including low contrast MRI brain images. The basic idea of this technique is to partition the input image histogram into two subhistograms based on a threshold which is obtained using Otsu's optimality principle. Then, bicriteria optimization problem is formulated to satisfy the aforementioned requirements. The subhistograms are modified by selecting optimal contrast enhancement parameters. Finally, the union of the modified subhistograms produce a contrast enhanced and details preserved output image. While developing an optimization problem, real coded genetic algorithm is applied to determine the optimal value of contrast enhancement parameters. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The quality of the enhanced brain image indicates that the image obtained after this method can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy and natural image quality evaluator.

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Thresholded and Optimized Histogram Equalization for contrast enhancement of images

Cited by 33 publications

References 13 publications

Automatic Contrast Enhancement by Transfer Function Modification

Automatic Contrast Enhancement by Transfer Function Modification

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images

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