CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR IMAGES WITH POOR LIGHTNING

International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
DOI : 10.5121/ijcsea.2013.3402 15
CONTRAST ENHANCEMENT TECHNIQUES USING
HISTOGRAM EQUALIZATION METHODS ON COLOR
IMAGES WITH POOR LIGHTNING
Dr. A. Sri Krishna1
, G. Srinivasa Rao2
and M. Sravya3
Department of Information Technology, R.V.R & J.C College of Engineering,
Guntur, Andhra Pradesh
atlurisrikrishna@yahoo.com1
gsr.rvr@gmail.com2
murukutlasravya@gmail.com3
ABSTRACT
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic
disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various
HE methods have been proposed. These methods preserves the brightness on the output image but, does not
have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which
decompose the image into several sub images, and classical HE method is applied to each sub image. The
algorithm is applied on various images and has been analysed using both objective and subjective
assessment.
KEYWORDS
Contrast enhancement, Histogram equalization, Multi Histogram Equalization, Multi-threshold selection.
1. INTRODUCTION
Image contrast enhancement [1] and [2] is a classical problem in image processing and computer
vision. Image enhancement is considered as a preprocessing step in many areas like video/image
processing applications [17] and [18] speech recognition, texture synthesis etc. Enhancement
techniques mainly fall into two broad categories: spatial domain methods and frequency domain
methods [15]. Spatial domain techniques are more popular than the frequency based methods,
because they are based on direct manipulation of pixels in an image. Myriad spatial domain
methods have been developed for visualizing the effect. Some of these methods uses simple linear
or non-linear intensity level transformation functions, whereas others use complex analysis of
different image features such as the edge and connected component information.
Contrast enhancement problem in digital images can be resolved using various methodologies,
but Histogram Equalization (HE) [15] technique is the widely used one. Histogram Equalization
method flattens the histogram and stretches the dynamic range of intensity values by using the
cumulative density function. However, there are major draw backs in Histogram Equalization
[10] especially when implemented to process digital images. Firstly, HE transforms the histogram
of the input image into a uniform histogram by distributing the entire range of gray levels
uniformly over the histogram of an image, with a mean value that is in the middle of gray level
range. Accordingly, the mean brightness of the output image is always at the middle – or near to
it in the case of discrete implementation. In the case of images with high and low mean brightness

16
value, there is a significant change in the view of the enhanced image. Secondly, histogram
equalization performs the enhancement based on the global content of the image. In other words,
histogram equalization highlights the borders and edges between different objects, but may
reduce the local details of these objects, and not adequate for local enhancement. Another
consequence for this mergence is the production of over enhancement and saturation artifacts
[16].
In order to overcome these drawbacks, variants of Histogram Equalization method are proposed.
These include Brightness BI Histogram Equalization Method (BBHE) [1],[5] and [11], Dualistic
sub image Histogram Equalization Method (DSIHE) [2] ,[6] and [14], Minimum Mean
Brightness Error BI Histogram Equalization Method (MMBEBHE) [3] and [14]. BBHE Method
divides the image in to two sub images based on the mean intensity value of the input image, and
Histogram Equalization technique is applied for each sub image separately. Based on the Equal
Area Property DSIHE method decomposes the image in to two sub images and Histogram
Equalization technique is applied for both the sub images. MMBEBHE method also decomposes
the image in to two sub images based on the intensity value that yields minimum Absolute Mean
Brightness Error (AMBE) [3]. Recursive Mean Separate Histogram Equalization Method
(RMSHE) [19] and [22] decomposes the input image recursively based on mean, up to a level r,
hence 2r
sub images are generated. These sub images are equalized individually to obtain the
desired result. Recursive Sub-Image Histogram Equalization (RSIHE) [9] and [22] is a
generalization of DSIHE, so the median-based histogram segmentation is performed more than
once. RSIHE uses the medians of sub histograms instead of the means of sub-histograms. Sub-
Region Histogram Equalization (CESRHE) [20] is a contrast enhancement method which
separates the intensity range of the histogram into k sub-regions and redistributes the pixel
intensities based on Sub-Intensity Range of output histogram. This method enhances the contrast
while preserving mean brightness. The weighted average multi segment histogram equalization
method [21] first smooths the global histogram and decomposes it into multiple segments via
optimal thresholds, and then HE is applied to each segment independently. These methods
outperform the conventional methods by producing enhanced image with details preserving
ability.
The paper is organized as follows. Section 2 presents a detailed description about different
Histogram Equalization Variants. Section 3 introduces two methodologies using Multi Histogram
Decomposition. Section 4 includes results and discussions. Finally, conclusion is presented in
Section 5.
2. HISTOGRAM EQUALIZATION VARIANTS
In this section, we briefly describe conventional histogram equalization [10] and its variant
methods. The CHE method was the base for all the other methods. The BBHE, DSIHE and
MMBEBHE methods use Conventional Histogram Equalization which will be later described in
this section. Notice that all the above CHE Variants decomposes the input image into sub images
based on some criteria, and each sub image is equalized separately. BBHE uses mean intensity
value, equal area property for DSIHE and MMBEBHE uses minimum AMBE as a criteria for
division of sub images.
2.1. Conventional Histogram Equalization Method
This sub section describes the CHE [15] for color images in detail, since this method is the core
of the present work. The goal of CHE method is to distribute uniformly the entire range of gray
levels over the histogram of an image, increasing the image contrast.

17
Let X ={X(i, j,l)} is an input image with L discrete gray levels {X0, X1, …., XL-1}, where X(i, j,l) is
the intensity of the image at the 3D position (i, j,l) and X(i, j,l) € {X0, X1,..,XL-1}, H(X) = {n0,
n1,…, nk,…, nL-1} is the histogram of the image and N is the total number of pixels in an image.
The probability density function (PDF) of the image is given in equation (1)
P(k)=nk/N (1)
where nk is the number of pixels whose gray level is Xk.
Cumulative distribution function (CDF) can be calculated using the Probability Density Function
(PDF) as shown in equation (2)
C(k)= (2)
Histogram equalization T[i] can be calculated using CDF by using equation (3)
T[i]={(G-1)/(a*b)}*c[i]; (3)
where G is the number of gray level in an image and a, b is number of rows and number of
columns in the image.
2.2. Brightness Preserving Bi-Histogram Equalization
BBHE [11] first divides the input histogram H(X) into two sub-histograms h1(x) and h2(x) by
using the mean xm, where h1(x) is associated with the gray levels {x0, x1,.., xm} and h2(x) is
associated with the gray levels {xm+1 , xm+2 ,…,xl-1}. Then it performs conventional histogram
equalization on h1(x) and h2(x) independently.
2.3. Dualistic Sub Image Histogram Equalization
The basic idea used in the DSIHE method is similar to by the BBHE method. Dividing the input
image into two sub-images and equalizes the histograms of the sub-images separately. Instead of
dividing the image based on its mean gray level, the DSIHE method divides the image based on
equal area property [13] i.e., both the sub images contain the equal number of pixels, being one
dark and one bright.
2.4. Minimum Mean Brightness Error BI Histogram Equalization Method
This method also decomposes the input image in to two sub images and Histogram Equalization
method is applied for both the sub images. In MMBEBHE we find the threshold level lt which
decomposes the image I into two sub-images I[0,lt ] and I[lt +1, L −1] , so that the minimum
brightness difference is achieved between the input image and the output image. The threshold
level lt is the value that yields minimum AMBE. Once the input image is decomposed, both the
sub images are enhanced separately using the Classical HE process
3. MULTI HISTOGRAM EQUALIZATION METHODS
Multi Histogram Equalization [7] and [8] is a technique in which the input image is divided in to
multiple sub images and then Histogram Equalization technique is applied for each sub image.
The intensity value that divides the image in to sub images is the optimal threshold [12] set. The
optimal threshold can be calculated using different methods but here we use two methods to

18
calculate the threshold set. The first algorithm calculates the optimal threshold set based on the
within class variance and divides the input image in to different sub images based on the
threshold set which is given in 3.1. The second algorithm divides the input image in to several sub
images based on the optimal threshold set generated using Otsu method as shown in 3.2. The
image is divided up to a scale r, so that 2r
sub images are generated. Where r can any predefined
value.
The main idea behind the Multi Histogram Equalization Methods is to find an optimal threshold
set Tk
= { t1
k
, t2
k
, .......,tk-1
k
}. The given input image is divided in to two sub images based on the
mean value. The optimal threshold set is then calculated for each sub image and the original
image is then divided in to K sub images I[ ls
1
, le
1
],.....,I[ls
k
, le
k
], where ls
1
referred to the
minimum threshold value in the first sub image and le
1
referred to the maximum threshold in the
first sub image. After dividing the image in to sub images apply Histogram Equalization Method
for all the sub images and combine the equalized images to obtain the desired result.
3.1 Minimum Variance Multi Histogram Equalization
Step 1: Calculate the histogram of the image.
Step 2: Based on the mean Brightness value, divide the histogram in to two classes.
Step 3: Calculate the within class variance for both the classes. Within class variance is
calculated as shown in equation (4)
Disc(i)=(i-Im) 2
* p(i) (4)
Where Im is the average of all the intensity values within each class.
p(i) is the probability density function and
‘i’ is the intensity value.
Step 4: Consider the intensity value that yeilds Minimum Within Class variance and divide each
class in to different sub images.
Step 5: Apply Histogram Equalization technique for all the sub images.
Otsu method [4] is used to perform histogram shape-based image thresholding. The method
assumes that the input image contains two classes of pixels or bi-modal histogram
i.e., foreground and background, and then calculate the threshold separating these two classes.
The extension of multi-level thresholding is referred to as the Multi Otsu method, in which
multiple threshold values are identified to divide the image in to multiple sub images.
3.2 Optimal Thresholding Multi Histogram Equalization
Step 1: Calculate the histogram of the image.
Step 2: Based on the mean Brightness value, divide the histogram in to two classes.
Step 3: Calculate the optimal threshold set using otsu[4] method.
Step 4: Based on the optimal threshold set divide the image in to different sub images.
Step 5: Apply Histogram Equalization technique for all the sub images.
4. RESULTS AND DISCUSSIONS
It is always desirable to have subjective and objective assessment to compare contrast
enhancement techniques. The subjective assessment is an assessment of quality where there is no
pre established measure or standard and is based solely on the opinion of the evaluator. The

19
quantitative measures are used for objective assessment. However we have used the Absolute
Mean Brightness Error (AMBE) and Mean Squared Error (MSE) as the quantitative measures.
The Absolute Mean Brightness Error is calculated as the absolute difference between the means
of the input and output image. MSE is a statistical measure of randomness that can be used to
characterize the texture of the input image.
4.1 Subjective Assessment on Color Images
Fig1 shows the original image and their corresponding contrast enhancement versions. The
various HE methods are compared. Fig1(a) shows the jar image which is poor lightened.
Generally HE method utilizes the dynamic range of the pixel values for maximum contrast. The
main drawback of HE method is over illumination which is observed in Fig1(b). By applying
BBHE method, the patterns in the jar are clearly visible than in HE method and is shown in
Fig1(c).
(a) (b) (c) (d)
(e) (f) (g)
Fig1: Results for jar: (a) Original Image (b) Enhanced Image by applying HE Method (c) Enhanced Image
by applying BBHE Method (d) Enhanced Image by applying DSIHE Method (e) Enhanced Image by
applying MMBEBHE Method (f) Enhanced Image by applying MVMHE Method (g) Enhanced
Image by applying OTMHE Method
In DSIHE Method, both the foreground and background parts are clearly visible than in the HE
and BBHE methods. It can be seen in Fig1(d). In MMBEBHE method, the contrast is improved
but the image does not have a natural look and it is shown in Fig 1(e). By applying the MVMHE
method, the image has a natural look with a good contrast but there are unnecessary artifacts in
the image. It is seen in Fig1(f). The OTMHE method preserves the brightness and increases the
contrast of the entire image when compared to other methods and is shown in Fig1 (g).

20
(a) (b) (c) (d)
(e) (f) (g)
Fig 2: Results for Lena: (a) Original Image (b) Enhanced Image by applying HE Method (c) Enhanced
Image by applying BBHE Method (d) Enhanced Image by applying DSIHE Method (e) Enhanced Image
by applying MMBEBHE Method (f) Enhanced Image by applying MVMHE Method (g) Enhanced
Fig.2 shows the Lena image and their corresponding contrast enhancement versions. The various
HE methods are compared. Fig2(a) shows the original brightened image. HE method increases
the contrast which results in over illumination of the image. This situation is observed in Fig2(b).
By applying BBHE method, the foreground part of the image is more clearly which is shown in
the Fig2(c). In DSIHE Method, the fore ground as well as the back ground has a pleasing look
and is shown in Fig2(d). In MMBEBHE method, the contrast is only improved and the image
does not have a natural look and is shown in Fig2(e). By applying the MVMHE method, the fore
ground has a clear look and the background part contains unnecessary patterns as shown in
Fig2(f).The OTMHE method preserves the brightness and increases the contrast of the entire
image. It is observed in Fig2(g).
(a) (b) (c) (d)

21
(e) (f) (g)
Fig3: Results for House: (a) Original Image (b) Enhanced Image by applying HE Method (c) Enhanced
by applying MMBEBHE Method. (f) Enhanced Image by applying MVMHE Method (g) Enhanced
Fig3 shows the House image and their corresponding contrast enhancement versions. The various
HE methods are compared. Fig3(a) is the original image with good contrast. HE method enhances
the image but the image does not have a natural appearance and shown in Fig3(b). By applying
BBHE method, the foreground part is brightened as in Fig3(c). In DSIHE method, both the
foreground and background are brightened more which can be observed in Fig3(d). In
MMBEBHE method, the contrast is improved but brightness is not preserved. Hence the output
image does not give a natural look which is shown in Fig3(e). By applying the MVMHE method,
the contrast is enhanced and the background appears brighter as in Fig3(f).The MWCVMHE
Method preserves the brightness and also improves the contrast of the image. It is observed in
Fig3(g).
Fig4 shows the Sea image and their corresponding contrast enhancement versions. The various
HE methods are compared. In Fig4 (a), there are very dark and bright regions. HE method
enhances the darker regions but the brighter regions are over enhanced and do not have pleasing
appearance. It is observed in Fig4 (b). By applying BBHE method, the brighter regions are over
enhanced and the image does not have a natural look which can be observed in the Fig4 (c).In
DSIHE method, the brighter regions are still over enhanced but the darker regions provides the
details of the background part and is shown in Fig4 (d). In MMBEBHE method, the details in the
darker region are hidden and only the brighter regions of the image are appeared as shown in Fig4
(e). By applying the MVMHE and OTMHE methods, the contrast is enhanced but there are some
non existing artifacts in the images which does not gives the natural appearance. It is observed in
Fig4 (f) and Fig4 (g).
(a) (b) (c) (d)

22
(e) (f) (g)
Fig 4: Results for Sea: (a) Original Image (b) Enhanced Image by applying HE Method (c) Enhanced
by applying MMBEBHE Method (f) Enhanced Image by applying MVMHE Method (g) Enhanced
4.1 Objective Assessment on Color Images
AMBE for all the intensity values with in an image is calculated.
AMBE=|Mi-Mo|
Where, Mi is the mean intensity value of input image
Mo is the mean intensity value of output image.
Mean squared error (MSE) method is used to evaluate the performance of a predictor or an
estimator. The mean squared error of the estimator or predictor is calculated as
MSE [T(Y); U] =E [(T(Y) – U)2
]
Where, T(Y) is the intensity value
U is the Mean intensity value of the input image.
Table 1: AMBE Values of Different Images
Method HE BBHE DSIHE MMBBHE MVMHE OTMHE
Jug 53.4377 49.92 64.1578 55.3254 19.9987 21.552
Lena 9.6505 12.873 14.8883 10.5379 11.3436 10.1969
Sea 100.6366 85.982 93.5739 73.982 11.1976 14.0923
House 42.7876 38.928 41.9275 54.641 17.9376 29.0906

23
Table 2: Mean Square Error Values of Different Images
The various HE methods to enhance the contrast have been applied on different color images
taken with different illumination. It has been observed that enhanced image using MVMHE
Method gives good contrast but generates additional patches at the brightest region of an image. It
has been observed that OTMHE Method results in generating natural look for the output image.
The patches generated using MVMHE method has been taken care in the case of OTMHE
method. The algorithm is basically used on the color images with poor lighting which helps us
identifying those objects which are completely invisible in the images. It is obvious from Table 1
and Table 2 that Multi Histogram Equalization Methods produce minimum MSE and AMBE
values when compared to all the other methods, which shows that these two methods are more
efficient when compared to the other methods.
5. CONCLUSION
Multi Histogram Equalization technique improves image contrast by brightness preserving and
generates natural looking images. An objective comparison among all the HE methods has been
performed using quantitative measures AMBE (Absolute Mean Brightness Error) and Mean
Square Error. It can be seen from the Tables that Multi Histogram Equalization Methods
preserves the brightness more efficiently than other methods and the Mean Square Error is also
reduced.
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Method HE BBHE DSIHE MMBBHE MVMHE OTMHE
Jug 0.0089 0.0096 0.0130 0.0230 0.0013 0.0004
Lena 0.0012 0.0011 0.0013 0.0041 0.0002 0.0003
House 0.0015 0.0013 0.0014 0.0019 0.0011 0.0009
Sea 0.0412 0.0315 0.0357 0.1398 0.0017 0.0011

24
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AUTHORS
Dr.A. Srikrishna received the AMIE degree in Electronics & Communication
Engineering from Institution of Engineers; Kolkata in 1990, M.S degree in Software
Systems from Birla Institute of Technology and Science, Pilani in 1994, M.Tech
degree in Computer Science from Jawaharlal Nehru Technological University (JNTU)
in 2003. She has worked as lecturer from (1991-95).She is working in RVR &JC
College of Engineering, Guntur from 13 years as Assistant Professor and Associate
Professor. She is pursuing her Ph.D from Jawaharlal Nehru Technological University
(JNTU) in Computer Science under the guidance of Dr.V.Vijaya Kumar. Her research
interest includes Image Processing and Pattern Recognition. She is Associate member
of IE (I) and member of CSI.
G.Srinivasa Rao graduated in B.E(CSE) from Marathwada university, India in the year
1989, received masters degree M.Tech (CSE) from JNTU Hyderabad in the year 2008,
M.s degree in software systems from Birla Institute of Technology and Science, Pilani in
1996 and he is pursuing PhD from JNTUH, Hyderabad. He has 23 years of teaching
Experience. currently he is working as Associate Professor in the department of
Information Technology, RVR & JC College of Engineering, Guntur. His research interest
includes Image and Signal Processing, algorithms and web technologies. He is a member
of CSI.
M.Sravya is Pursuing her B.Tech Graduation in Information Technology, in RVR & JC
College of Engineering, Chowdavaram, Guntur Andhra Pradesh, India. She has been
actively presenting papers and participating in student technical Symposium seminars at
National Level. Her area of interest includes Image Processing, Cloud Computing and
Pattern Recognition.

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