IRJET- Real Time Implementation of Bi-Histogram Equalization Method on Android Platform

© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1555
Real Time Implementation of Bi-Histogram Equalization Method on
Android Platform
Shiva Sumanth Reddy1, Anil kumar B2, Manjunath D R3
1,2,3Computer Science and Engineering, DSATM, Bangalore,Karnataka
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Abstract - Android is an open-source platform developed
by Google and Open handset alliance. Image Processing
on Android based mobile devices is an emerging field in
today’s World. Android also comes with a vast library of
useful functions, including functions for user interfaces,
image/bitmap manipulation, and camera controls.
Histogram equalization is widely used for contrast
enhancement in a variety of applications due to its
simplicity 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.
This paper presents the implementation of Brightness
Preserving Histogram Equalization in Android Platform.
The drawback of histogram equalizationcan beovercome
by using the above mentioned method. This paper also
shows the output on Android platform.
Key Words: Histogram Equalization, Image Processing,
Image Acquisition, Android, BBHE.
1. INTRODUCTION
Histogram equalization is the one of the well-known
methods for enhancing the contrast of given images in
accordance with the sample distribution of an image. Useful
applications of the histogram equalization scheme include
medical image processing and radar image processing. In
general, histogram equalization flats thedensitydistribution
of the resultant image and enhances the contrast of the
image as a consequence, since histogram equalizationhasan
effect of stretching dynamic range.
The BBHE firstly decomposes an input image into two sub-
images based on the mean of the input image. Oneofthesub-
images is the set of samples less than or equal to the mean
whereas the other one is the set of samples greater than the
mean.
The BBHE firstly decomposes an input image into two sub-
images based on the mean of the input image. Oneofthesub-
images is the set of samples less than or equal to the mean
whereas the other one is the set of samples greater than the
mean. Then the BBHE equalizes the sub-images
independently based on their respectivehistogramswiththe
constraint that the samples.
Then the BBHE equalizes the sub-images independently
based on their respective histograms. In other words, one of
the sub-images is equalized over the range up to the mean
and the other sub-image is equalized over therangefromthe
mean based on the respective histograms.Thus,theresulting
equalized sub-images are bounded by each otheraroundthe
input mean, which has an effect of preserving mean
brightness.
The architecture of the image processing in the Android
mobile is shown in below.
Fig -1: Enhancement Methodology
Image Acquisition refers to the capturing of image data by a
particular sensor or data repository. Once the image data is
acquired, Pre-Processing often includes removing of noise
from the acquired image data. Histogram Equalization is the
process of enhancing the image.
1.1 Motivation
Image processing on mobile phones is a new and exciting
field with many challenges due to limited hardware and
connectivity. Phones with cameras, powerful CPUs, and
memory storage devicesare becomingincreasinglycommon.
The need for benchmarking basic image processing routines
such as: addition, convolution, thresholding and edge
detection is important for comparison of systems. With this
informationdevelopersandresearcherscan design complex
computer vision and image processing applications while
being aware of the current state of the art limitations and
bottlenecks on mobile phones.
1.2 Goals
The goal of this paper is to focus on Image Acquisition,
Pre-Processing through implementing image noise removal
Image
Acquisition
Pre-
processing
Bi-Histogram
Equalization
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 08 | Aug 2018 www.irjet.net p-ISSN: 2395-0072

algorithms; implementing Histogram Equalization and
Histogram Equalization based Enhancement method on
Android based mobilesusing the availableAndroidSoftware
Development Kit (SDK).
2. Approach and Challenges
The Android operating system is preferable for
benchmarking due to its recent growth and popularity. Few
of the hardware manufacturersaree.g.HTC,Motorola,LGand
Samsung. The Android operating system is supported and a
partof the Open Handset Alliance. This alliance positionskey
manufacturers, cellularprovidersand the Androidoperating
system in a collaborative environment which has caused
large growth since October 2008 when the first Android
mobile phone was released.
Few challenges when implementing Android mobile devices
with Android OS includes architecting software and
optimizing code for
a. Memory limitations
b. CPU limitations
c. Image Quality limitations
3. Histogram Equalization Algorithm
Input: An Image file, Row M, Column N
Output: Enhanced Image after histogram Equalization
Steps:
1. Let X = { X ( i ,j ) } denote a given image composed of L
discrete gray levels denoted as {XO,X1,.,. . ,X L - 1 } , where X (
i , j ) represents an intensity of the image at the spatial
location ( i , j ) and X ( i , j ) {XO,X1,.,. . ,X L - 1 } . For a given
imageX, the probability density function p (X k ) isdefinedas
=
for k = 0 , 1 , . . . , L - 1, where nk represents the number of
times that the level Xk appears in the input image X and n is
the total number of samples in the input image.
2. Based on the probability density function, we define the
cumulative density function as
Where, Xk = x, for k = 0, l , . . . , L - 1. Note that C(XL- l) = 1 by
definition. Histogram equalization is a scheme that mapsthe
input image into the entire dynamic range, (Xo, XL-1), by
using the cumulative density function as a transform
function.
3. Define a transform function f(x) based on the cumulative
density function as
=
4. Then the output imageof the histogramequalization,Y={Y
(i,j) } , can be expressed as
Y
=
4. BBHE ALGORITHM
Input: An Image file, Row M, Column N
Output: Enhanced Image after histogram Equalization
Steps:
Denote by Xm the mean of the image X and assume that
, ,Based on the
mean, the input image is decomposedintotwosub-imagesXL
and XU as
2. The probability density functions of the sub-image XL and
XU as
and
5. Results
Fig -2: Input image of Forest

Fig -3: Output Image of Forest
Fig -4: Input Image of Under water Fish
Fig -5: Output Image of Under water Fish
Fig -6: Input Image of Furniture
Fig -7: Output Image of Furniture
6. Conclusion
In this paper we have used two different image
enhancement methods that are popularly used in image
understanding studies. Our results show that the images
after enhancement have better visibility than the original
images. This paper shows that the processing can be applied
even for the real time image that is the image captured from
mobile. This paper also avoids the flattening property of
Histogram Equalization by using the Bi-Histogram
Equalization.
REFERENCES
[1]. J.S Lim, Two-Dimensional Signal and Image processing,
Prentice Hall, Englewood Cliffs, New Jersey 1990.
[2]. R.C Gonzalez and P. Wints, Digital Image Processing, 3rd
Edition, Addison-Wesley Publishing Co., Reading,
Massachusetts.

[3].Y. Li, Wang, and D. Y. Yu, “Application of adaptive
histogram equalization to x-ray chest image,” Proc. Of the
SPIE, pp. 513-514, Vol. 2321 1994.
[4].Y.T. Kim, “Contrast Enhancement using Brightness
Preserving Bi–Histogram Equalization”. IEEE Transactions
on Consumer Electronics, Volume 43, No.1, 1997.
[5]. Tutorial on Using Android for Image ProcessingProjects,
EE368 Digital Image Processing, Spring 2010, Linux Version

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