Histogram equalization

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Histogram equalization

  1. 1. ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print) IJECT Vol. 2, Issue 1, March 2011 Histogram Equalization Techniques For Image Enhancement 1 Rajesh Garg, 2Bhawna Mittal, 3Sheetal Garg H.I.T., Sonepat, Haryana, India 1,2 3 S.M.Hindu Sr.Sec.School, Sonepat, Haryana, IndiaAbstractVarious enhancement schemes are used for enhancing animage which includes gray scale manipulation, filtering andHistogram Equalization (HE). Histogram equalization is oneof the well known imaget enhancement technique. It becamea popular technique for contrast enhancement because thismethod is simple and effective. In the latter case, preservingthe input brightness of the image is required to avoid thegeneration of non-existing artifacts in the output image.Although these methods preserve the input brightness on the Fig.1: Image enhancementoutput image with a significant contrast enhancement, theymay produce images with do not look as natural as the input B. Adaptive Histogram Equalization methodones. The basic idea of HE method is to re-map the gray levels This is an extension to traditional Histogram Equalizationof an image. HE tends to introduce some annoying artifacts technique. It enhances the contrast of images by transformingand unnatural enhancement. To overcome these drawbacks the values in the intensity image I. Unlike HISTEQ, it operatesdifferent brightness preserving techniques are used which on small data regions (tiles), rather than the entire image. Eachare covered in the literature survey. Comparative analysis of tiles contrast is enhanced, so that the histogram of the outputdifferent enhancement techniques will be carried out. This region approximately matches the specified histogram. Thecomparison will be done on the basis of subjective and objective neighboring tiles are then combined using bilinear interpolationparameters. Subjective parameters are visual quality and in order to eliminate artificially induced boundaries.computation time and objective parameters are Peak signal- The contrast, especially in homogeneous areas, can be limitedto-noise ratio (PSNR), Mean squared error (MSE), Normalized in order to avoid amplifying the noise which might be presentAbsolute Error (NAE), Normalized Correlation, Error Color and in the image.Composite Peak Signal to Noise Ratio (CPSNR). C. Dualistic sub-image histogram equalization methodKeywords This is a novel histogram equalization technique in which theContrast enhancement, Histogram equalization, PSNR, MSE, original image is decomposed into two equal area sub-imagesNAE, CPSNR, Visual quality. based on its gray level probability density function. Then the two sub-images are equalized respectively. At last, weI. Introduction get the result after the processed sub-images are composedOut of the five senses – sight, hearing, touch, smell and taste into one image. In fact, the algorithm can not only enhance– which humans use to perceive their environment, sight is the image visual information effectively, but also constrain thethe most powerful. Receiving and analyzing images forms a original images average luminance from great shift. This makeslarge part of the routine cerebral activity of human beings it possible to be utilized in video system directly.throughout their waking lives. In fact, more than 99% of theactivity of the human brain is involved in processing images C. Dynamic histogram equalization for image contrastfrom the visual cortex. A visual image is rich in information. enhancementConfucius said, “A picture is worth a thousand words.” [1] Image It employs a partitioning operation over the input histogramEnhancement is simple and most appealing area among all to chop it into some sub histograms so that they have nothe digital image processing techniques. The main purpose of dominating component in them. Then each sub-histogram goesimage enhancement is to bring out detail that is hidden in an through HE and is allowed to occupy a specified gray level rangeimage or to increase contrast in a low contrast image. Whenever in the enhanced output image. Thus, a better overall contrastan image is converted from one form to other such as digitizing enhancement is gained by DHE with controlled dynamic rangethe image some form of degradation occurs at output. of gray levels and eliminating the possibility of the low histogram components being compressed that may cause some part ofA. Image Enhancement the image to have washed out appearance.Image enhancement is among the simplest and most appealingareas of digital image processing. Basically, the idea behind II. Backgroundenhancement techniques is to bring out detail that is obscured, One of the first applications of digital images was in theor simply to highlight certain features of interest in an image. newspaper industry, when pictures were first sent by submarineA familiar example of enhancement is shown in Fig.1 in which cable between London and New York. Introduction of thewhen we increase the contrast of an image and filter it to remove Bartlane cable picture transmission system in the early 1920sthe noise "it looks better." It is important to keep in mind that reduced the time required to transport a picture across theenhancement is a very subjective area of image processing. Atlantic from more than a week to less than three hours.Improvement in quality of these degraded images can be Specialized printing equipment coded pictures for cableachieved by using application of enhancement techniques. transmission and then reconstructed them at the receiving end.w w w. i j e c t. o r g   International Journal of Electronics & Communication Technology  107
  2. 2. IJECT Vol. 2, Issue 1, March 2011 ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print)Some of the initial problems [2] in improving the visual quality B. Equal area Dualistic sub-image histogram equalizationof these early digital pictures were related to the selection of method:printing procedures and the distribution of intensity levels. Algorithm Steps:Although the methods just cited involve digital images, they Suppose image X is segmented by a section with gray levelare not considered digital image processing results in the of X=Xe and the two sub-images are Xl and Xu, so we havecontext of our definition because computers were not involvedin their creation. Thus, the history of digital image processing ִ X=XlU XU. Here X L=is intimately tied to the development of the digital computer. Infact, digital images require so much storage and computational … (1)power that progress in the field of digital image processing has It is obvious that sub image XL is composed by gray level ofbeen dependent on the development of digital computers and {X 0,X1,……… Xe-1}, while sub image X U is composed ofof supporting technologies that include data storage, display, {Xe,Xe+1,………….Xl-1}. The aggregation of the original images’ grayand transmission. level distribution probability is decomposed into {p0,p1,……… pe-1}III. Implementation and {pe,pe+1,………….pl-1} correspondingly. The corresponding Compare all these techniques on the basis of performance cumulative distribution function will beparameters in objective and subjective manner. These arethe merits on the bases of that I will compare above defined CL ( X k) = , k = 0 ,1 , … … . e-1... (2)techniques. CU (Xk) = , k=e,e+1,….. L-1A. Contrast Limited Adaptive Histogram Equalization Based on the cumulative distribution function, the transformmethod: functions of the two sub images’ histogram are equalizedAlgorithm Steps: below.1. Obtain all the inputs: Image, Number of regions in row and F L( X k) = X 0+ ( X e-1- X 0) c ( X k) , k = 0 , 1 , … . e - 1 … (3) column directions, Number of bins for the histograms used in building image transform function (dynamic range), Clip FU(Xk)=Xe+(Xl-1-Xe)c(Xk), k=e,e+1,………..L-1 limit for contrast limiting (normalized from 0 to 1)2. Pre-process the inputs: Determine real clip limit from At last result of dualistic sub image histogram is obtained after the normalized value if necessary, pad the image before the two equalized sub images are composed into one image. splitting it into regions Suppose Y denotes the processed image then3. Process each contextual region (tile) thus producing gray ִ Y={Y(i,j)}= f L (X L ) U f U (X U ) …. (4) level mappings: Extract a single image region, make a histogram for this region using the specified number of bins, clip the histogram using clip limit, create a mapping (transformation function) for this region4. Interpolate gray level mappings in order to assemble final CLAHE image: Extract cluster of four neighbouring mapping functions, process image region partly overlapping each of the mapping tiles, extract a single pixel, apply four mappings to that pixel, and interpolate between the results to obtain the output pixel; repeat over the entire image. Fig.3: Flow chart for DSIHEFig.2: Flow chart for CLAHE108  International Journal of Electronics & Communication Technology w w w. i j e c t. o r g
  3. 3. ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print) IJECT Vol. 2, Issue 1, March 2011C. Dynamic histogram equalization for image contrast 3. Contrast:enhancement: Contrast defines the difference between lowest and highestAlgorithm Steps: intensity level. Higher the value of contrast means more1. Histogram Partition : DHE partitions the histogram based on difference between lowest and highest intensity level.local minima. At first, it applies a one-dimensional smoothingfilter of size 1 x 3 on the histogram to get rid of insignificant 4. Visual Qualityminima. Then it makes partitions (sub-histograms) taking the By looking at the enhanced image, one can easily determineportion of histogram that falls between two local minima (the the difference between the input image and the enhancedfirst and the last non-zero histogram components are considered image and hence, performance of the enhancement techniqueas minima). Mathematically, if m0, m1, …, mn are (n+1) gray is evaluated.levels (GL) that correspond to (n+1) local minima in the imagehistogram, then the first sub-histogram will take the histogramcomponents of the GL range [m0, m1], the second one will take[m1+1, m2] and so on.These histogram partitioning helps toprevent some parts of the histogram from being dominatedby others.2. Gray Scale Allocation: For each sub-histogram, DHE allocatesa particular range of GLs over which it may span in outputimage histogram. This is decided mainly based on the ratio ofthe span of gray levels that the sub-histograms occupy in theinput image histogram.Here the straightforward approach isSpani= mi-mi-1rangei=where, spani = dynamic GL range used by sub-histogram i ininput image.mi = ith local minima in the input image histogram.rangei = dynamic gray level range for sub-histogram i in outputimage.The order of gray levels allocated for the sub-histograms inoutput image histogram are maintained in the same order asthey are in the input image, i.e., if sub-histogram i is allocatedthe gray levels from [istart, iend], then istart = (i-1)end + 1 and iend=istart + rangei. For the first sub-histogram, j, jstart = r0.3. Histogram Equalization : Conventional HE is applied to eachsub-histogram, but its span in the output image histogramis allowed to confine within the allocated GL range that isdesignated to it. Therefore, any portion of the input imagehistogram is not allowed to dominate in HE.D. Metrics for Gray Scale Images: Fig. 4: Flow chart for DHE1. Peak-signal-to-noise-ratio (PSNR): IV. Tool to be used:PSNR is the evaluation standard of the reconstructed image In this thesis for implementation of techniques MATLAB 7.0.2quality, and is important measurement feature. PSNR is version is used. In that image processing toolbox is used.measured in decibels (dB) and is given by: MATLAB® is a high-performance language for technical  2  computing.PSNR = 10log  255 MSE    V. Experimental Resultswhere the value 255 is maximum possible value that can To verify the efficacy of the proposed method, obtained afterbe attained by the image signal. Mean square error (MSE) is following the Different enhancement Algorithms for gray scaledefined as Where M*N is the size of the original image. Higher images. After the comparison tables, a graphical representationthe PSNR value is, better the reconstructed image is. has also been done for a quick analysis of results. All the techniques have been tested for all the assumed standard test2. Absolute mean brightness error (AMBE): images.In this paper three techniques are used for Gray ScaleIt is the Difference between original and enhanced image and Image enhancement which are CLAHE, DSIHE and DHE.is given as A. Results of test image “Rice”AMBE= Fig.5 shows the visual quality of real image “Rice” and theWhere E(x)= average intensity of input image E(y)=average enhanced image using three different image enhancementintensity of enhanced image techniques. The performances of these techniques are evaluated in terms of PSNR, AMBE and Contrast.w w w. i j e c t. o r g   International Journal of Electronics & Communication Technology  109
  4. 4. IJECT Vol. 2, Issue 1, March 2011 ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print) (a) Histogram of Image a) Original Imageb) CLAHE Image (b) CLAHE Histogram (c ) Histogram of DHEc) DSIHE Image (d) DSIHE Histogram Fig. 6: Equalized Histograms for Image “Rice” as shown in image a, b, c, d as original, CLAHE, DHE, DSIHE Respectively.d) DHE Image Table 1: Comparison of Various Parameters for “Rice” Image:Fig. 5: Enhanced Result of real image as shown in image a, Parameter AMBE Contrast PSNRb, c, d. Technique CLAHE 13.8521 23.5878 0.0366B. Histograms of test image “Rice” for different DSIHE 4.9081 33.8767 0.0327enhancement algorithmsFig.6 shows respective Histograms of test image “rice” using DHE 13.0886 12.1438 0.1107Different image enhancement techniques. Anyone can make comparison of parameter AMBE (Absolute mean brightness error) for different image enhancement techniques. The value of AMBE should be as small as possible110  International Journal of Electronics & Communication Technology w w w. i j e c t. o r g
  5. 5. ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print) IJECT Vol. 2, Issue 1, March 2011which indicates that difference between original and enhanced Proceedings.image should be minimum. Therefore in terms of AMBE, DSIHE [9] Md. Foisal Hossain, Mohammad Reza Alsharif, “Imagetechnique gives best results as AMBE is taken in negative. Enhancement Based on Logarithmic Transform CoefficientNow considering PSNR, CLAHE gives better output as it is cleared and Adaptive Histogram Equalization”, 2007 Internationalfrom the formula that PSNR should be as high as possible so Conference on Convergence Information Technology, IEEEthat noise content should be lower than signal content. 2007. [10] J. Alex Stark “Adaptive Image Contrast EnhancementVI. Conclusion and Future Scope Using Generalizations of Histogram Equalization”, IEEEIn this Paper, a frame work for image enhancement based Transactions on Image Processing, Vol. 9, No. 5, Mayon prior knowledge on the Histogram Equalization has been 2000.presented. Many image enhancement schemes like Contrast [11] Wang Yuanji. Li Jianhua, Lu E, Fu Yao, Jiang Qinzhong,limited Adaptive Histogram Equalization (CLAHE), Equal area “Image Quality Evaluation Based On Image Weighteddualistic sub-image histogram equalization (DSIHE), Dynamic Separating Block Peak Signal To Noise Ratio”, IEEE Int.Histogram equalization (DHE) Algorithm has been implemented Conf. Neural Networks & Signal Processing, Nanjing,and compared. The Performance of all these Methods has been China, December 14-17, 2003.analyzed and a number of Practical experiments of real time [12] Rafael C. Gonzalez, Richard E. Woods, “Digital Imageimages have been presented. From the experimental results, it Processing”, 2nd edition, Prentice Hall, 2002.is found that all the three techniques yields Different aspects for [13] Stephen M. Pizer, R. Eugene Johnston, James P. Ericksen,different parameters. In future, for the enhancement purpose Bonnie C. Yankaskas, Keith E. Muller, “Contrast-Limitedmore images can be taken from the different application fields Adaptive Histogram Equalization Speed and Effectiveness”,so that it becomes clearer that for which application which ”, IEEE Int. Conf. Neural Networks & Signal Processing,particular technique is better both for Gray Scale Images and Nanjing, China, December 14-17, 2003.colour Images. Particularly, for colour images there are not [14] Rafael C. Gonzalez, Richard E. Woods, “Digital Imagemany performances measurement parameter considered. Processing”, 2nd edition, Prentice Hall, 2002.So, new parameters can be considered for the evaluation of [15] A. K. Jain, “Fundamentals of Digital Image Processing”.enhancement techniques. New colour models can also be Englewood Cliffs, NJ: Prentice-Hall, 1991.chosen for better comparison purpose. Optimization of various [16] A. Zagzebski, “Essentials of Ultrasound Physics”. St. Louis,enhancement techniques can be done to reduce computational Missouri: Mosby, 1996.complexity as much as possible. Rajesh Garg received his B.E. degree inReferences Electronics & Comm. from Hindu College of[1] S. Lau, “Global image enhancement using local engineering, Sonipat, Haryana, in 2006 and information,” Electronics Letters, vol. 30, pp. 122–123, pursuing the M-Tech. (part-time) degree in Jan. 1994. Electronics & Comm. From M.M. University,[2] J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, Mullna (Ambala). Presently, he is engaged B. Brenton, “Evaluation of the effectiveness of adaptive in teaching, as a lecturer in Electronics & histogram equalization for contrast enhancement,” IEEE Comm. Department in Hindu institute of Transactions on Medical Imaging, pp. 304-312, 1988. Technology, Sonepat since 2006.[3] Yu Wan, Qian Chen, Bao-Min Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Transactions Consumer Bhawna Mittal received her B.E. degree Electron., vol. 45, no. 1, pp. 68-75, 1999. in Electronics & Comm. from North[4] Yeong-Taeg Kim, “Contrast enhancement using brightness Maharashtra University in 1998 and preserving bi-histogram equalization,” IEEE Trans. M-Tech. (part-time) degree in Electronics & Consumer Electronics, vol. 43, no. 1, pp. 1-8, 1997. Comm. from Rajasthan University, Udaipur[5] M. Abdullah-Al-Wadud, Md. Hasanul Kabir, M. Ali Akber in 2007. She was teaching as lecturer Dewan, Oksam Chae, “A dynamic histogram equalization in S.J.P.P.,Damla in 1999 to 2000.From for image contrast enhancement”, IEEE Transactions. 2000 onwards she worked as a Lecturer Consumer Electron., vol. 53, no. 2, pp. 593- 600, May in Electronics & Comm. Department at 2007. Hindu institute of Technology, Sonepat then promoted as Sr.[6] K. Wongsritong, K. Kittayaruasiriwat, F. Cheevasuvit, Lecturer in Electronics & Comm. Department at Hindu institute K. Dejhan, A. Somboonkaew, “Contrast enhancement of Technology, Sonepat in 2007.Presently,she is engaged in all using multipeak histogram equalization with brightness the academic activities of the institute. preserving”, Circuit and System, 1998, IEEE APCCAS 1998. The 1998 IEEE Asia-Pacific Conference on 24-27 Sheetal Garg received her B.Sc. degree Nov. 1998, pp. 455-458, 1998. in Computer Science from G.V.M. Girls[7] Y. Wang, Q. Chen, B. Zhang, Soong-Der Chen, and Abd. College, Sonipat, Haryana, in 2002 and Rahman Ramli, “Minimum mean brightness error bi- M.C.A degree from Kurukshetra University, histogram equalization in contrast enhancement”, IEEE Kurukshetra in 2005. Presently, she Transactions Consumer Electron. vol. 49, no. 4, pp. 1310- is engaged in teaching, as a lecturer 1319, Nov. 2003. in Computer Science Department in[8] WANG Zhiming, TAO Jianhua, “A Fast Implementation of S.M.Hindu Sr.Sec.School, Sonepat since Adaptive Histogram Equalization”, IEEE 2006, ICSP 2006 2005.w w w. i j e c t. o r g   International Journal of Electronics & Communication Technology  111

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