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Real Time Bi-Histogram Equalization on Android
1.
© 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 ---------------------------------------------------------------------***--------------------------------------------------------------------- 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
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© 2018, IRJET
| Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1556 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 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
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| Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1557 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. 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
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| Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1558 [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 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
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