An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299 J. Sci. Res. 3 (1), 43-50 (2011)

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Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

Mathematical Problems in Engineering ◽

10.1155/2014/848615 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Haidi Ibrahim ◽

Seng Chun Hoo

Keyword(s):

Contrast Enhancement ◽

Histogram Equalization ◽

Processing Technique ◽

Image Contrast ◽

Local Contrast ◽

Image Contrast Enhancement ◽

Enhancement Method ◽

Equalization Technique ◽

Histogram Bins ◽

Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

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An Efficient Image Contrast Enhancement Method using Sigmoid Function and Differential Evolution

Journal of Advanced Engineering and Computation ◽

10.25073/jaec.202043.267 ◽

2020 ◽

Vol 4 (3) ◽

pp. 162

Author(s):

Kim-Ngan Nguyen-Thi ◽

Ha Che-Ngoc ◽

Anh-Thy Pham-Chau

Keyword(s):

Differential Evolution ◽

Contrast Enhancement ◽

Image Enhancement ◽

Image Features ◽

Image Contrast ◽

Sigmoid Function ◽

Creative Commons ◽

De Algorithm ◽

Image Contrast Enhancement ◽

Enhancement Method

Image enhancement is an adjusting process to make an image more appropriate for certain applications. The contrast enhancement is one of the most frequently used image enhancement methods. In this study, we introduce a new image contrast enhancement method using a link between sigmoid function and Differential Evolution (DE) algorithm. DE algorithm is performed to identify the parameters in sigmoid function so that they can maximize the measure of contrast. The experimental results show that the proposed method not only retains the original image features but also enhances the contrast effectively. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement

Mathematical Problems in Engineering ◽

10.1155/2014/302732 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

P. Jagatheeswari ◽

S. Suresh Kumar ◽

M. Mary Linda

Keyword(s):

Image Processing ◽

Contrast Enhancement ◽

Dynamic Range ◽

Median Filter ◽

Histogram Equalization ◽

Input Image ◽

Enhancement Technique ◽

Image Contrast Enhancement ◽

The Mean ◽

Plateau Limit

The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

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Image Pixel Contrast Enhancement Using Enhanced Multi Histogram Equalization Method

Ingénierie des systèmes d information ◽

10.18280/isi.260110 ◽

2021 ◽

Vol 26 (1) ◽

pp. 95-101

Author(s):

Gutta Srinivasa Rao ◽

Atluri Srikrishna

Keyword(s):

Contrast Enhancement ◽

Image Enhancement ◽

Satellite Image ◽

Histogram Equalization ◽

Image Contrast ◽

The Poor ◽

Image Contrast Enhancement ◽

Poor Lighting ◽

Equalization Method ◽

Image Datasets

Image Enhancement methods produce various sorts of problems, for example, unnatural impacts, over-improvement, and these downsides become increasingly unmistakable in improving dull Images. Histogram Equalization (HE) method is a straightforward and generally utilized Image contrast enhancement procedure. The fundamental task of HE is it changes the contrast of the Image. To perform this task, different HE techniques have been proposed. These techniques protect the brightness or contrast on the final Image that doesn't have a characteristic look. To overcome the drawbacks of HE, Enhanced Multi Histogram Equalization (EMHE) technique is proposed, which divide the Image into a few sub images and again these sub images are divided into sub-images, and traditional HE strategy is applied to each sub Image for getting better results. The improvement is brought about by repetitive data present in sub-pixel moves between relating Lightroom (LR) Images of a similar scene. The principal phase of the development incorporates Image enrollment of LR Images utilizing known parameters and geo-referencing methods for manufactured and genuine information individually. The proposed development of M-HE Images has been assessed on the LR Images obtained from satellite Image datasets to exhibit the clarity of the images by enhancing the contrast on the poor lighting images.

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Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

Computational Intelligence and Neuroscience ◽

10.1155/2017/6029892 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Liyun Zhuang ◽

Yepeng Guan

Keyword(s):

Image Enhancement ◽

State Of The Art ◽

Image Database ◽

Histogram Equalization ◽

Input Image ◽

Public Image ◽

Enhancement Method ◽

Mean And Variance ◽

The Mean ◽

Histogram Bins

This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image.

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