Survey of Contrast Enhancement Techniques based on Histogram Equalization
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paper_title: Improving the visual quality of abdominal magnetic resonance images using histogram equalization

paper_content:

Histogram equalization is one of the well known enhancement techniques commonly used for medical images. Although easy to be implemented, global histogram equalization sometime produces unnecessary visual deterioration, such as saturation in intensity values. One of the available improved versions of histogram equalization is brightness preserving dynamic histogram equalization (BPDHE). This method has been proposed to be used in consumer electronics. In this paper, we investigate whether this method is also suitable to be employed in medical application or not, especially for abdominal magnetic resonance images. Our preliminary results show that in general, BPDHE can significantly improve the quality of medical data.
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paper_title: Contrast enhancement using multipeak histogram equalization with brightness preserving

paper_content:

Contrast enhancement of an image can efficiently be performed by histogram equalization. However, the global histogram equalization will cause an effect on brightness saturation in some almost homogeneous area. Therefore, multipeak histogram equalization has been proposed. Each detected peak of the histogram is independently equalized. The effect of brightness saturation can be eliminated and the perceptibility improved. By using the histogram equalization method, the output image can preserve the mean value brightness of the input image.
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paper_title: Color image enhancement using brightness preserving dynamic histogram equalization

paper_content:

Histogram equalization (HE), although one of the most popular techniques used for digital image enhancement, is not very suitable to be implemented directly in consumer electronics, such as television, because this method tends to produce an output with saturation effect. To overcome this weakness, it is suggested that the mean intensity of the input image be maintained in the output image. Previously, we proposed a method known as brightness preserving dynamic histogram equalization (BPDHE) which can fulfill this requirement for grayscale images. In this paper, we present several possibilities to extend this method for color images.
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paper_title: Brightness preserving weight clustering histogram equalization

paper_content:

Histogram equalization (GHE) is a simple and widely accepted method for contrast enhancement. Although there are extensions of GHE that can preserve the brightness of the original image better than the original method, these extensions sometimes fail to enhance the visualization of the original image. Therefore, we propose a new method called "Brightness Preserving Weight Clustering Histogram Equalization" (BPWCHE) that can simultaneously preserve the brightness of the original image and enhance visualization of the original image. BPWCHE assigns each non-zero bin of the original image's histogram to a separate cluster, and computes each cluster's weight. Then, to reduce the number of clusters, we use three criteria (cluster weight, weight ratio and widths of two neighboring clusters) to merge pairs of neighboring clusters. The clusters acquire the same partitions as the result image histogram. Finally, transformation functions for each cluster's sub-histogram are calculated based on the traditional GHE method in the new acquired partitions of the result image histogram, and the sub-histogram's gray levels are mapped to the result image by the corresponding transformation functions. We showed experimentally that BPWCHE can preserve image brightness and enhance visualization of images more effectively than GHE and other brightness preserving methods.
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paper_title: Contrast enhancement using brightness preserving bi-histogram equalization

paper_content:

Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
---
paper_title: Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

paper_content:

Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.
---
paper_title: Multilevel component-based histogram equalization for enhancing the quality of grayscale images

paper_content:

Contrast enhancement is an important step in almost every image processing and computer vision application. Different applications demand different approaches to achieve the required level of enhancement. In consumer electronics, for example, any enhancement method should be able to enhance both global and local details but not at the expense of deterioration in image quality. In this paper we present our preliminary work on the development of a new method for image contrast enhancement called Multilevel Component-Based Histogram Equalization. This new method is simple and effective, and exploits the capabilities of the classical histogram equalization approach, multiple graylevel thresholding, and connected component analysis to allow for both global and local contrast enhancements with minimum distortion in image appearance. Visual and quantitative evaluation revealed the capability of the new method to achieve this goal.
---
paper_title: A Dynamic Histogram Equalization for Image Contrast Enhancement

paper_content:

In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.
---
paper_title: A Weighting Mean-Separated Sub-Histogram Equalization for Contrast Enhancement

paper_content:

In the last decade, improvement of the visual image quality has been actively developed using the contrast enhancement techniques due to the need to better show the visual information contained within the image for all vision-based systems. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the precise histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.
---
paper_title: Image enhancement based on equal area dualistic sub-image histogram equalization method

paper_content:

Histogram equalization is a simple and effective image enhancing technique. But in some conditions, the luminance of an image may be changed significantly after the equalizing process, this is why it has never been utilized in a video system in the past. A novel histogram equalization technique, equal area dualistic sub-image histogram equalization, is put forward in this paper. First, the image is decomposed into two equal area sub-images based on its original probability density function. Then the two sub-images are equalized respectively. Finally, we obtain the results after the processed sub-images are composed into one image. The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.
---
paper_title: Contrast enhancement using brightness preserving bi-histogram equalization

paper_content:

Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
---
paper_title: Image enhancement based on equal area dualistic sub-image histogram equalization method

paper_content:

Histogram equalization is a simple and effective image enhancing technique. But in some conditions, the luminance of an image may be changed significantly after the equalizing process, this is why it has never been utilized in a video system in the past. A novel histogram equalization technique, equal area dualistic sub-image histogram equalization, is put forward in this paper. First, the image is decomposed into two equal area sub-images based on its original probability density function. Then the two sub-images are equalized respectively. Finally, we obtain the results after the processed sub-images are composed into one image. The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.
---
paper_title: Minimum mean brightness error bi-histogram equalization in contrast enhancement

paper_content:

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE) to provide maximum brightness preservation. BBHE separates the input image's histogram into two based on input mean before equalizing them independently. This paper proposes to perform the separation based on the threshold level, which would yield minimum absolute mean brightness error (AMBE - the absolute difference between input and output mean). An efficient recursive integer-based computation for AMBE has been formulated to facilitate real time implementation. Simulation results using sample image which represent images with very low, very high and medium mean brightness show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE. Besides, MMBEBHE also demonstrate comparable performance with BBHE and DSIHE when come to use the sample images show in [Yeong-Taeg Kim, February 1997] and [Yu Wan et al., October 5 1999].
---
paper_title: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

paper_content:

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.
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paper_title: Brightness preserving histogram equalization with maximum entropy: a variational perspective

paper_content:

Histogram equalization (HE) is a simple and effective image enhancing technique, however, it tends to change the mean brightness of the image to the middle level of the permitted range, and hence is not very suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. This paper proposes a novel extension of histogram equalization, actually histogram specification, to overcome such drawback as HE. To maximize the entropy is the essential idea of HE to make the histogram as flat as possible. Following that, the essence of the proposed algorithm, named brightness preserving histogram equalization with maximum entropy (BPHEME), tries to find, by the variational approach, the target histogram that maximizes the entropy, under the constraints that the mean brightness is fixed, then transforms the original histogram to that target one using histogram specification. Comparing to the existing methods including HE, brightness preserving bi-histogram equalization (BBHE), equal area dualistic sub-image histogram equalization (DSIHE), and minimum mean brightness error bi-histogram equalization (MMBEBHE), experimental results show that BPHEME can not only enhance the image effectively, but also preserve the original brightness quite well, so that it is possible to be utilized in consumer electronic products.
---
paper_title: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

paper_content:

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.
---
paper_title: Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement

paper_content:

This paper proposes a new histogram equalization method, called RSWHE (recursively separated and weighted histogram equalization), for brightness preservation and image contrast enhancement. The essential idea of RSWHE is to segment an input histogram into two or more sub-histograms recursively, to modify the sub-histograms by means of a weighting process based on a normalized power law function, and to perform histogram equalization on the weighted sub-histograms independently. RSIHE (recursive sub-image histogram equalization) and RMSHE (recursive mean separate histogram equalization) are some methods similar to RSWHE, but they do not carry out the above weighting process. We show that compared to other existent methods, RSWHE preserves the image brightness more accurately and produces images with better contrast enhancement.
---
paper_title: A Dynamic Histogram Equalization for Image Contrast Enhancement

paper_content:

In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.
---
paper_title: Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

paper_content:

Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.
---
paper_title: Automatic thresholding of gray-level using multistage approach

paper_content:

A multistage approach is presented for thresholding document images, along with its application. The proposed method is based on two stages. Global thresholding is used in the first stage to give a preliminary result. A second stage then refines the threshold value based on local spatial characteristics of the regions formed in the first stage. It automatically customizes the thresholding of regions that have specific and consistent characteristics but are different to other regions in the image. This technique works well for both simple images, in which the background and foreground are distinct and separable and complex images containing multiple regions with different shading/textures. A typical application is postal envelope analysis. The results of evaluation show significant improvement compared to several other global and local thresholding techniques.
---
paper_title: Multilevel component-based histogram equalization for enhancing the quality of grayscale images

paper_content:

Contrast enhancement is an important step in almost every image processing and computer vision application. Different applications demand different approaches to achieve the required level of enhancement. In consumer electronics, for example, any enhancement method should be able to enhance both global and local details but not at the expense of deterioration in image quality. In this paper we present our preliminary work on the development of a new method for image contrast enhancement called Multilevel Component-Based Histogram Equalization. This new method is simple and effective, and exploits the capabilities of the classical histogram equalization approach, multiple graylevel thresholding, and connected component analysis to allow for both global and local contrast enhancements with minimum distortion in image appearance. Visual and quantitative evaluation revealed the capability of the new method to achieve this goal.
---
paper_title: A Weighting Mean-Separated Sub-Histogram Equalization for Contrast Enhancement

paper_content:

In the last decade, improvement of the visual image quality has been actively developed using the contrast enhancement techniques due to the need to better show the visual information contained within the image for all vision-based systems. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the precise histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.
---
paper_title: Minimum mean brightness error bi-histogram equalization in contrast enhancement

paper_content:

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE) to provide maximum brightness preservation. BBHE separates the input image's histogram into two based on input mean before equalizing them independently. This paper proposes to perform the separation based on the threshold level, which would yield minimum absolute mean brightness error (AMBE - the absolute difference between input and output mean). An efficient recursive integer-based computation for AMBE has been formulated to facilitate real time implementation. Simulation results using sample image which represent images with very low, very high and medium mean brightness show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE. Besides, MMBEBHE also demonstrate comparable performance with BBHE and DSIHE when come to use the sample images show in [Yeong-Taeg Kim, February 1997] and [Yu Wan et al., October 5 1999].
---
paper_title: Multilevel component-based histogram equalization for enhancing the quality of grayscale images

paper_content:

Contrast enhancement is an important step in almost every image processing and computer vision application. Different applications demand different approaches to achieve the required level of enhancement. In consumer electronics, for example, any enhancement method should be able to enhance both global and local details but not at the expense of deterioration in image quality. In this paper we present our preliminary work on the development of a new method for image contrast enhancement called Multilevel Component-Based Histogram Equalization. This new method is simple and effective, and exploits the capabilities of the classical histogram equalization approach, multiple graylevel thresholding, and connected component analysis to allow for both global and local contrast enhancements with minimum distortion in image appearance. Visual and quantitative evaluation revealed the capability of the new method to achieve this goal.
---
paper_title: A Weighting Mean-Separated Sub-Histogram Equalization for Contrast Enhancement

paper_content:

In the last decade, improvement of the visual image quality has been actively developed using the contrast enhancement techniques due to the need to better show the visual information contained within the image for all vision-based systems. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the precise histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.
---

Title: Survey of Contrast Enhancement Techniques based on Histogram Equalization

Section 1: INTRODUCTION
Description 1: This section introduces the concept of image enhancement and its purpose, with a focus on Histogram Equalization (HE) and its drawbacks.

Section 2: HISTOGRAM EQUALIZATION (HE)
Description 2: This section explains the basic Histogram Equalization method, including its probability density function and cumulative density function, and provides details on its performance and limitations.

Section 3: Brightness Preserving Bi-Histogram Equalization (BBHE)
Description 3: Discusses the BBHE method, which divides the histogram into two parts based on the input mean brightness value, and equalizes these parts independently to improve brightness preservation.

Section 4: Dualistic Sub-Image Histogram Equalization (DSIHE)
Description 4: Examines the DSIHE method that decomposes the image into two sub-images based on Shannon's entropy to enhance brightness without significant shifts.

Section 5: Minimum Mean Brightness Error Bi-HE Method (MMBEBHE)
Description 5: Describes the MMBEBHE method that minimizes brightness difference by finding a threshold level that decomposes the image into two sub-images, performing HE on each.

Section 6: Recursive Mean-Separate HE Method (RMSHE)
Description 6: Details the RMSHE method, an extension of BBHE, which decomposes the image recursively and equalizes each sub-image independently to better preserve brightness.

Section 7: MEAN BRIGHTNESS PRESERVING HISTOGRAM EQUALIZATION (MBPHE)
Description 7: This section outlines MBPHE methods, which can be divided into bisections MBPHE and multi-sections MBPHE, and their effectiveness in preserving mean brightness.

Section 8: DYNAMIC HISTOGRAM EQUALIZATION (DHE)
Description 8: Discusses the DHE technique, which controls the effects of traditional HE by dividing the histogram and applying HE on sub-histograms to enhance contrast without losing image details.

Section 9: BRIGHTNESS PRESERVING DYNAMIC HISTOGRAM EQUALIZATION (BPDHE)
Description 9: Examines the BPDHE method that extends HE to produce output images with mean intensity similar to the input, preserving mean brightness while enhancing contrast.

Section 10: MULTILEVEL COMPONENT BASED HISTOGRAM EQUALIZATION (MCBHE)
Description 10: Explores the MCBHE algorithm, which combines multiple techniques to enhance both global and local contrast with minimal distortion.

Section 11: WEIGHTING MEAN-SEPARATED SUB-HISTOGRAM EQUALIZATION (WMSHE)
Description 11: Describes the WMSHE method, which separates the histogram into sub-images based on a weighting mean function and applies HE to each, optimizing contrast enhancement.

Section 12: DISCUSSION
Description 12: This section provides a comparative study of the different histogram equalization-based methods, discussing their effectiveness, advantages, and side effects.