ArticlePDF Available

Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

Authors:

Abstract

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.
School of Electrical Engineering and Computer Science
Kyungpook National Univ.
Contrast Enhancement using Recursive
Contrast Enhancement using Recursive
Mean
Mean
-
-
Separate Histogram Equalization for
Separate Histogram Equalization for
Scalable Brightness Preservation
Scalable Brightness Preservation
Soong
Soong
-
-
Der
Der
Chen,
Chen,
Abd
Abd
.
.
Rahman
Rahman
Ramli
Ramli
IEEE Transactions on Consumer Electronics,
IEEE Transactions on Consumer Electronics,
Vol.49, No. 4, pp. 1301
Vol.49, No. 4, pp. 1301
-
-
1309, 2003
1309, 2003
2/ 28
Contrast enhancement
Histogram equalization
Preserving the original brightness to avoid
annoying artifacts
Bright preserving Bi-Histogram Equalization
Not handling well to avoid annoying artifacts
Recursive Mean-Separate Histogram
Equalization
Separating each new histogram further based on
respective mean
Abstract
3/ 28
Histogram equalization
Base on the probability distribution of the input
gray levels
Significant change of the brightness
Not commonly using in consumer electronics such
as TV
Necessary of the brightness preservation
Bi-histogram equalization(BBHE)
Separating the input image’s histogram into two
based on its mean
Introduction
Introduction
4/ 28
Dualistic Sub-Image Histogram Equalization
Separating the histogram based on gray level with
cumulative probability density equal to 0.5
Proposed method
Separation each new histogram further based on
their respective means
Output image’s mean brightness converging to the
input image’s
5/ 28
Typical histogram equalization
Given image , probability density function
Cumulative density function
Histogram equalization
Histogram equalization
n
n
Xp
k
k
=)(
(1)
imageinput in the samples ofnumber total theis
appears level that the timesofnumber therepresents where
1,...,1,0For
n
Xn
L
k
k
k
=
=
=
k
j
j
Xpxc
0
)()(
(2)
6/ 28
Define a transform function
Output image of HE,
Significant change in brightness
Unnatural enhancement
(3)
)()()(
010
xcXXXxf
L
+
=
)},({ jiY
=
}),(),(({
)(
=
=
jiXjiXf
f
(4)
(5)
7/ 28
Fig. 1.(a) Original image arctic hare. Fig. 1.(b) Result of HE.
8/ 28
Fig. 2.(a) Original image girl. Fig. 2.(b) Result of HE.
9/ 28
Fig. 3.(a) Original image jet. Fig. 3.(b) Result of HE.
10 / 28
Brightness preserving Bi-Histogram Equalization
Decompose input Image into two sub-image
Respective PDF
(6)
UL
and
=
}),(,),(),({ = jiXXjiXjiX
m
}),(,),(),({ >= jiXXjiXjiX
m
(8)
(7)
L
k
L
kL
n
n
Xp =)(
U
k
U
kU
n
n
Xp =)(
(10)
(9)
11 / 28
Respective CDF
Transform function
=
=
k
j
jLL
Xpxc
0
)()(
+=
=
k
mj
jUU
Xpxc
1
)()(
(11)
(12)
)()()(
00
xcXXXxf
LmL
+
=
)()()(
111
xcXXXxf
UmLmU +++
+
=
(14)
(13)
12 / 28
Output image of BBHE
)()(
)},{{
UULL
ff
jiY
=
=
}),()),(({)(
LLLL
jiXjiXff =
}),()),(({)(
UUUU
jiXjiXff =
(15)
(16)
(18)
(17)
13 / 28
Analysis on the brightness change by the BBHE
Suppose to continuous random variable
Result image of HE, uniform density
Mean brightness of the output image of the HE
and
)/(1)(
01
XXxp
L
+
(19)
2
)()(
01
01
1
0
1
0
XX
dx
XX
x
dxxxpYE
L
X
X
L
X
X
L
L
+
=
=
=
(20)
(21)
(22)
14 / 28
Mean brightness of the output of the BBHE
Function of the input mean brightness
Preserve the brightness
})()({
2
1
)Pr()()Pr()()(
mm
mmmm
XEXE
XXEXXEE
>+=
>>+=
(23)
2/)()(
0 mm
XXXXE +=
2/)()(
1
+=>
Lmm
XXXXE
2/)()(
Gm
XXE
+
=
2/)( where
10
+
=
LG
XXX
(24)
(25)
(26)
(27)
15 / 28
Fig. 4.(a) Result of BBHE of image arctic hare.
Fig. 4.(b) Result of DSIHE of image arctic hare
16 / 28
Fig. 4.(a) Result of BBHE of image girl.
Fig. 4.(b) Result of DSIHE of image girl.
17 / 28
Fig. 4.(a) Result of BBHE of image jet.
Fig. 4.(b) Result of DSIHE of image jet.
18 / 28
Separate the resulting histograms again
based on their respective means
Generalization of HE and BBHE
Output mean E(Y) of typical HE,
Output mean E(Y) of BBHE,
Recursive mean
Recursive mean
-
-
separate histogram equalization
separate histogram equalization
GL
XXXE
=
+
=
2/)()(
10
2/)()(
Gm
XXE
+
=
0
=
r
1
=
r
(28)
(29)
19 / 28
Fig. 7. Histogram before and after HE
or equivalently, RMSHE, .
Fig. 8. Histogram before and after BBHE
or equivalently, RMSHE, .
0
=
r
1
=
r
20 / 28
RMSHE with recursion level,
2
=
r
Fig. 9. Histogram before and after RMSHE, .
2
=
r
21 / 28
Mean of the two new histogram,
muml
XX and
==
m
m
m
X
X
X
X
X
X
ml
dxxxp
dxxp
dxxxp
X
0
0
0
)(2
)(
)(
==
1
1
1
)(2
)(
)(
L
m
L
m
L
m
X
X
X
X
X
X
mu
dxxxp
dxxp
dxxxp
X
2
1
)()( where
1
0
==
L
m
m
X
X
X
X
dxxpdxxp
(30)
(31)
(32)
22 / 28
Formulation of the output mean
(33)
)}( )(
)( )({
4
1
)Pr()(
)Pr()(
)Pr()(
)Pr()()(
mumum
mmlml
mumu
mummum
mmlmml
mml
XEXXE
XXEXE
XXE
XXXXE
XXXXE
XXEE
>++
+=
>>+
<+
<+
=
23 / 28
Similar discussion to obtain (22)
(34)
}3{
4
1
}2{
4
1
}]2/)(2[]2/){[(
4
1
]}2/)[(]2/)[(
]2/)[(]2/){[(
4
1
)(
10
1
0
mG
mmG
mmumlL
Lmumum
mmlml
XX
XXX
XXXXX
XXXX
XXXXE
+=
++=
++++=
++++
+++=
24 / 28
From (30) and (31)
Increasing to three times as much as the
weight of middle gray level,
(35)
m
X
X
X
X
X
X
X
X
mlmu
X
dxxxp
dxxxpdxxxp
dxxxpdxxxp
XX
L
L
m
m
L
m
m
=
=
+=
+
=
+
)(
)()(
2
)(2)(2
2
1
0
1
1
0
0
m
X
G
X
25 / 28
Output mean for RMSHE recursion level
Larger , converge to the input mean,
(35)
)(YE
nr
=
]2/)[(
2/))12(()( ,
4/)3()( ,2
2/)()( ,1
)( ,0
n
mGm
n
Gm
n
Gm
Gm
G
XXX
XXEnr
XXEr
XXEr
XEr
+=
+==
+==
+==
=
=
K
)(YE
n
m
X
26 / 28
Result from RMSHE with
Increasing the brightness preservation
More natural enhancement
Results and discussions
Results and discussions
2
=
r
Fig. 10. Result of RMSHE r=2 of Image arctic hare.
27 / 28
Fig. 12. Result of RMSHE r=2 of
Image of jet
Fig. 11. Result of RMSHE r=2 of
Image of girl
28 / 28
Recursive mean-separate histogram
equalization
Generalization of HE and BBHE in term of
brightness preservation
Recursively separating the input histogram based
on the mean
Future work
Proper mechanism to automate the selection of
the recursion level, r that give optimum output
Conclusion
Conclusion
... It was also capable to handle the problems which incurred in HE and DSIHE. Chen and Ramli [5] proposed a generalized way of HE and BBHE named as RMSHE to get the better results with scalable brightness preservation. It is achieved using the recursive mean separation process. ...
... For comparison with state-of-the-art methods, we have used the following seven images 'F16', 'Lena', 'Butterfly', 'Fish', Portofino' and two medical images, MI-1 and MI-2 as test images. To show the efficacy of the proposed method, various evluation parameters are obtained and compared with existing methods such as HE, BHE, RMHE, BBHE, DSIHE [41], RMSHE [5], MMBEBHE [6], RSIHE [30], DHE, BHEPL, ESIHE [31], MMSICHE, BHEMHB [34], EASHE, MVSIHE [43]. The parameters which have been used for justification of contrast enhancement, brightness preservation and also its naturalness are contrast, PSNR, Entropy, AMBE and SSIM. ...
Article
Full-text available
The present paper focuses on the contrast enhancement of an image using linear regression-based recursive sub-histogram equalization. The histogram of an image is partitioned into two non-overlapping sub-histograms using the mean intensity of the image. A set of points is constructed for each sub-histogram, considering gray level (intensity) as the abscissa and its corresponding count as the ordinate of the point. Then the method of least squares is used for fitting lines of regression for these sets of points in each sub-histogram. With the help of the regression line and histogram, intervals are created in each segmented partition. This process of creating intervals gives more intervals as compared to the Recursive Sub-Image Histogram Equalization (RSIHE) and the Mean and Variance-based Sub Image Histogram Equalization methods (MVSIHE). For qualitative and quantitative analysis of the proposed method, the experiments are performed on a set of test images, including medical and non-medical images. The evaluated results are presented in terms of various evaluation metrics. For medical images, the mean opinion score is also evaluated with the proposed method and other recent methods. The comparison with state-of-the-art methods shows the efficacy of the proposed method for enhancement.
... Gupta et al. [5], Kim et al. [6], and Sim et al. [7] employ adaptive gamma correction (AGC) and histogram equalization (HE) approaches to enhance the luminosity of the image without the occurrence of a gamut problem. Histogram modification based on recursive procedures, optimization is given by Wang et al. [8], Arici et al. [9], and Chen et al. [10], but these approaches are not optimal due to their recursive nature. A weighted distribution is applied to image regions through AGC by Cheng et al. [11]. ...
Preprint
Full-text available
In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis. Our objective is to introduce a CAD system with a fresh approach. Retinal image quality improvement is attempted in two phases. The retinal image preprocessing phase improves the brightness and contrast of the image through quantile based histogram modification. It is followed by the image enhancement phase, which involves multi scale morphological operations using image specific dynamic structuring elements for the retinal structure enrichment. Graph based retinal image features in terms of Local Graph Structures (LGS) and Graph Shortest Path (GSP) statistics are extracted from various directions along with the statistical features from the enhanced retinal dataset. WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function. The performance of the WNN classifier is compared with multilayer perceptron neural networks with various datasets. The results show our approach is superior to the existing approaches.
... In the second step, BBHE is applied to the fuzzy domain, and the fuzzy plane is mapped back to the gray level image in the final step. Chen and Ramli [26] proposed recursive mean separate histogram equalization (RMSHE), which partitions the histogram of a given image recursively. Each segment is equalized independently, and the contrast-enhanced output is obtained by adding all of the segments together. ...
Article
Full-text available
Enhancement of mammogram images against low-contrast and poor illumination is still a challenge for researchers. Focusing on such issues, this manuscript presents a two-stage enhancement technique for mammogram images. The first stage of the image enhancement deals with illumination control using the corrective-adaptive gamma correction (CAGC) approach. In the second stage, contrast enhancement operation on the luminosity-controlled image is incorporated. In order to enhance visual perception against low-contrast, the combined application of discrete wavelet transform (DWT) and singular value decomposition (SVD) is incorporated. The experimentation of the proposed technique was performed over a publicly available mini-MIAS dataset. The proposed technique is evaluated on various quantitative parameters such as Pearson correlation coefficient (PCC), universal image quality index (IQI), structural similarity index measurement (SSIM), contrast improvement index (CII), average mean brightness error (AMBE), and mean absolute error (MAE) and obtain the average values of 0.996, 0.912, 0.921, 1.098, 15.732, and 15.624 that are promising results as compared to the other traditional methods. This study also compares the proposed technique with state-of-art methods and achieves better performance, resulting in significant improvement in contrast enhancement and local information preservation of mammogram images.
... However, the enhancement effect of this method for low illumination image in extremely dark environment is not ideal, and there is a strong oversaturation phenomenon. Similarly, recursive mean segmentation histogram equalization (RMSHE) [10] and brightness-preserving dynamic histogram equalization (BPDHE) [11] can keep the average brightness of the input image when processing the image output, but they may still not generate images with ideal natural appearance. In 2012, Khan et al. proposed an algorithm based on brightness maintenance, namely weighted average multi-segment histogram equalization (WAMSHE) [12]. ...
Article
This paper proposes a new method for low-light image enhancement with balancing image brightness and preserving image details, this method can improve the brightness and contrast of low-light images while maintaining image details. Traditional histogram equalization methods often lead to excessive enhancement and loss of details, thereby resulting in an unclear and unnatural appearance. In this method, the image is processed bidirectionally. On the one hand, the image is processed by double histogram equalization with double automatic platform method based on improved cuckoo search (CS) algorithm, where the image histogram is segmented firstly, and the platform limit is selected according to the histogram statistics and improved CS technology. Then, the sub-histograms are clipped by two platforms and carried out the histogram equalization respectively. Finally, an image with balanced brightness and good contrast can be obtained. On the other hand, the main structure of the image is extracted based on the total variation model, and the image mask with all the texture details is made by removing the main structure of the image. Eventually, the final enhanced image is obtained by adding the mask with texture details to the image with balanced brightness and good contrast. Compared with the existing methods, the proposed algorithm significantly enhances the visual effect of the low-light images, based on human subjective evaluation and objective evaluation indices. Experimental results show that the proposed method in this paper is better than the existing methods.
... RMSHE performs histogram decomposition recursively, where each new histogram is separated further based on its individual mean value [24]. RMSHE provides scalable brightness preservation to overcome the disadvantages of the previous techniques. ...
Article
Full-text available
Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.
... Several methods related to HE has been proposed in current years to overcome HE method constraints. Bi Histogram equalization with a Plateau Limit (BHPL) [7], Brightness preserving Bi Histogram Equalization (BPBHE) [8], Dualistic Sub Image Histogram Equalization (DSHE) [9], Dynamic Histogram Equalization (DHE) [10], Recursive Mean-Separate Histogram Equalization (RMHE) [11], Brightness Preserving Weight Clustering HE (BPWCHE) [12], and Minimum Mean Brightness Error Bi-Histogram Equalization (MMBHE) [13]. The Contrast Limited Adaptive Histogram Equalization (CLAHE) [14], on the other hand, advances the equalization process by efficiently enhancing the local information by selecting local histogram transfer functions. ...
Article
Full-text available
Retinal imaging can be used to identify a variety of common eye and cardiac disorders. However, owing to non-uniform or poor illumination and low contrast, low-quality retinal fundus medical images are ineffective for diagnostic, particularly in computerized image analysis systems. The article proposes an effective image enhancement method for improving the luminosity and contrast of color retinal fundus images. To begin, the input color retinal fundus image is transformed to HSV (Hue, Saturation, and Value) color model, which separates the luminance channel (V) from the other color elements hue (H) and saturation (S). Then, on the luminosity channel (V), a new JND-based adaptive gamma correction method is utilized to improve the luminance of fundus images. After that, contrast is improved in the luminance component in the L*a*b* color space, employing a novel contrast enhancement technique that employs several layers of CLAHE (contrast limited adaptive histogram equalization). These two techniques substantially improve the overall luminance and contrast in retinal images while preserving the average brightness, keeping an original appearance, and maximizing the entropy of the input retinal fundus images. Experiments on a broad range of fundus images are performed to assess the proposed scheme's performance both qualitatively and quantitatively. Substantial objective evaluation indicates that the proposed scheme surpasses state-of-the-art enhancement techniques in terms of edge preservation index, entropy, a measure of enhancement, contrast ratio, and enhancement metrics. This retinal fundus image enhancement method can be employed to support ophthalmologists in effectively inspecting for retinal disorders and developing more accurate computerized image analysis for medical diagnosis.
... Common image enhancement methods include contrast enhancement method, histogram equalization, noise smoothing and sharpening. Typical techniques include histogram equalization [1,6,7,11,19,38], Retinex algorithm [2,12,21,22,29,30], homomorphic filtering algorithm [3,37], gamma correction technology [15], retina-based scheme and transformation-based scheme [16,20]. ...
Article
Full-text available
The introduction of speckle noise in the process of digital holographic reconstruction is inevitable. Meanwhile, the quality of the reconstructed images are seriously lower than that of the original images, affecting the visual perception. The removal of speckle noise is an internationally recognized conundrum. Currently, scholars have not proposed a better method to remove speckle noise, which hinders the further development of digital holography technology. As a result, reducing speck noise in digital holographic reconstruction and enhancing the quality of reconstructed images have become important research topics. Based on the characteristics of speckle noise in reconstructed images, this paper proposes a new method for the first time by combining the concepts of image segmentation, guided filtering, and filter reconstruction, which can significantly improve the image quality within a reasonable time. By comparison with other state-of-the-art methods, the proposed method perform excellently in terms of detail preservation and background noise suppression of the target image. Finally, a holographic reconstruction image quality enhancement system is developed, integrating the physical experiment of digital holographic reconstruction with the image quality enhancement algorithm. The simple and convenient operation of the system provides great help for non-algorithm physics researchers. Additionally, it is also the first holographic reconstruction image enhancement system with a good effect and has considerable market application value.
Article
The histogram equalization approach, which is employed for image enhancement, reduces the number of pixel intensities, resulting in detail loss and an unnatural impression. This research proposes a strategy to improve the contrast of an image based on its nature. The images' statistical parameters mean, median and kurtosis are extracted and utilized to classify them into uniform and non-uniform background images. Initially, the image is decomposed using a multilevel decomposition based on the l1−l0 minimization model to extract its significant edge information. Later, the retrieved edge information is employed in proper histogram equalization to produce an improved result. Variational histogram equalization is proposed here to overcome the problem of over-amplification and artifacts in the homogeneous zone caused by histogram spikes in the uniform background images. Non-uniform background images are enhanced via two-dimensional histogram equalization, which takes advantage of the joint occurrences of edge information and pixel intensities in the low contrast image. The proposed technique is tested on the five databases: CSIQ, TID2013, LOL, DRESDEN, and FLICKR. SD, CII, DE, NIQE, and AMBE are the performance metrics used to validate the algorithm's effectiveness. Experimental analysis shows that the proposed technique outperforms the other algorithms, including deep learning architectures in high CII, SD, DE, and low NIQE values.
Article
In this paper, a novel histogram modification-based bi histogram equalization (HE) approach for contrast enhancement on digital images is presented. At first a power-logarithm transformation function is used to change the histogram of the input image. The logarithm operation reduces the input histogram’s excessive peaks, while the power function restores the histogram structure. The adjusted histogram is then separated into two sub-histograms at the threshold limit, using the input image’s minimum intensity and standard deviation. Sub-histograms are clipped based on their individual plateau limit parameters, which are based on the value median of the individual sub-histogram, to control the over-enhanced outcomes. The clipped pixels are then redistributed evenly across the histogram’s non-empty bins. Finally, the intended outcome is achieved by applying the HE procedure on the updated individual sub-histogram. Simulation results show that the proposed HE approach successfully improves the visual quality of the images. Quantitative measurements such as entropy, feature similarity index measure (FSIM), spectral residual similarity index measure (SR-SIM), gradient magnitude similarity deviation (GMSD), visual saliency-induced index (VSI) and multi scale structural similarity index measure (MS-SSIM) efficiently confirm the effectiveness of the suggested method when compared with the existing enhancement techniques. Furthermore, a comparative study of the different approaches is performed using a dataset of 300 images, demonstrating the superiority of the suggested method over the other state-of-the-art techniques.
Article
Full-text available
In computed tomography (CT) imaging, a low‐quality CT scan, or more precisely, a noisy low contrast CT image, may provide insufficient information for visual interpretation of impacted regions. The purpose of this article is to propose a novel optimal morphological transform‐based method for improving the noise and contrast of CT images in the wavelet domain. The low‐quality CT image is first transformed using the optimized morphology transform method, which determines the optimal value of the parameter included in the fundamental morphology transformation equation. The parameter's optimum value is determined using the particle swarm optimization (PSO) method. The proposed approach then employs the discrete wavelet transform (DWT) to decompose the input and transformed CT image into higher and lower sub‐bands. Following that, the lower sub‐bands are modified by applying the correction factor via singular value decomposition (SVD). The input image's higher sub‐band (HH) is denoised using an edge map (EM)‐based method. This produces enhanced CT images with modified lower and higher sub‐band. Finally, the modified sub‐bands and remaining unprocessed higher frequency sub‐bands are processed using the inverse discrete wavelet transform (IDWT), resulting in an enhanced CT image. Experiments and validations are conducted on a CT images obtained from a publicly available database to assess the proposed scheme's effectiveness qualitatively and quantitatively. Significant quantitative study shows that the proposed approach surpasses existing image enhancement approaches in terms of signal‐to‐noise ratio, discrete entropy, enhancement measurement, and contrast ratio. The proposed method generates higher‐quality CT images, which is advantageous for disease inspection and diagnosis.
Article
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 original input brightness may necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such drawback of the 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 will be 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 much natural enhancement that can be utilized in consumer electronic products.
Article
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
Article
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
Image Enhancing Method Using Men-Separate Histogram Equalization United States Patent, Patent No
  • Young
  • Kim
  • Cho
Young-tack Kim and Yong-hun Cho, " Image Enhancing Method Using Men-Separate Histogram Equalization, " United States Patent, Patent No. 5,963,665, Oct 5, 1999.
Image Enhancement Based On Equal Area Dualistic Sub-Image Histogram Equalization Method Fig. 11 Result of RMSHE r = 2 of image girl Fig. 10 Result of RMSHE r = 2 of image arctic hare Fig. 12 Result of RMSHE r = 2 of image jet S
  • Yu Wan
  • Qian Chen
  • R Bao-Min Zhang Chen
  • Ramli
Yu Wan, Qian Chen and Bao-Min Zhang., " Image Enhancement Based On Equal Area Dualistic Sub-Image Histogram Equalization Method, " IEEE Trans Consumer Electronics, vol. 45, no. 1, pp. 68-75, Feb. 1999. Fig. 11 Result of RMSHE r = 2 of image girl Fig. 10 Result of RMSHE r = 2 of image arctic hare Fig. 12 Result of RMSHE r = 2 of image jet S.-D. Chen and R. Ramli: Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation
United States Patent, Patent No. 6,049,626 Soong-Der Chen (M'2002) was born in October 6
  • Yeong
  • Kim
Yeong-taeg Kim " Image Enhancing Method And Circuit Using Men-Separate/Quantized Mean Separate Histogram Equalization And Color Compensation, " United States Patent, Patent No. 6,049,626, Apr 11, 2000. Soong-Der Chen (M'2002) was born in October 6, 1973 in Kuala Lumpur, Malaysia. He received his B.E. in Electronics/Computer (1997), and M.Sc in Electronic Imaging (2000) in the Department of Computer and Communication Engineering in Universiti Putra Malaysia, Serdang, Malaysia. Currently he is pursuing his Ph.D degree in the same university. His research interest includes image compression and image enhancement
Image Enhancing Method Using Men-Separate Histogram Equalization
  • Y.-T Kim
  • Y.-H Cho