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43 JEST-M, Vol.1, Issue 2, 2012endothelial cell, which might be of low contrast and somewhatblurred. Reducing the noise and blurring and increasing thecontrast range could enhance the image. The original imagemight have areas of very high and very low intensity, whichmask details. An adaptive enhancement algorithm revealsthese details. Adaptive algorithms adjust their operation basedon the image information (pixels) being processed. In this casethe mean intensity, contrast, and sharpness (amount of blurremoval) could be adjusted based on the pixelintensitystatistics in various areas of theThe following shows the histogram equalizationof an arbitrary histogram.Methods of Image Enhancement1) Histogram EqualizationThis method usually increases the global contrastof many images, especially when the usable dataof the image is represented by close contrastvalues. Through this adjustment, the intensitiescan be better distributed on the histogram. Thisallows for areas of lower local contrast to gain ahigher contrast. Histogram equalizationaccomplishes this by effectively spreading out themost frequent intensity values.The method is useful in images with backgroundsand foregrounds that are both bright or both dark.In particular, the method can lead to better viewsof bone structure in x-ray images, and to betterdetail in photographs that are over or underexposed.A key advantage of the method is that itis a fairly straightforward technique and aninvertible operator. So in theory, if the histogramequalization function is known, then the originalhistogram can be recovered. A disadvantage ofthe method is that it is indiscriminate. It mayincrease the contrast of background noise, whiledecreasing the usable signal.Fig2-histogram equalization2) Gamma Correction Gamma correction, gamma nonlinearity, gammaencoding, or often simply gamma, is the name ofa nonlinear operation used to code and decodeluminance or tristimulus values in video or stillimage systems. [1] Gamma correction is, in thesimplest cases, defined by the following powerlawexpression: where the input and output values are nonnegativereal values, typically in a predeterminedrange such as 0 to 1. A gamma value issometimes called an encoding gamma, and theprocess of encoding with this compressivepower-law nonlinearity is called gammaKavitha Ramasamy

45 JEST-M, Vol.1, Issue 2, 2012Fig-4 plot of Homomorphic filteringRetinex MethodsIII.RETINEX THEORYThe original aim of the theory is a computationalmodel of human vision, but it has also been used and extendedfor machine vision. In theory, most versions of Retinex arerobust with respect to slowly spatially varying illumination,R ( x,y)although testing on real images has been limited to sceneswhere the illumination has been controlled to be quite uniform.Nonetheless, the varying illumination component of this workis both interesting and important. In Retinex based methods,varying illumination is discounted by assuming that smallspatial changes in the responses are due to changes in theillumination whereas large changes are due to surfaceschanges. The goal of Retinex is to estimate the lightness of asurface in each channel by comparing the quantum catch ateach pixel or photoreceptor to the value of some statisticoriginally the maximumÑ found by looking at a large areaaround the pixel or photoreceptor. The ratios of thesequantities (or their logarithms) are the descriptors of interest,and thus the method implicitly assumes the diagonal model.Implementation of Retinex ModelIn this paper, we will implement SSR, MSR, andMSRCR with gain/offset. We adjust the gain/offsetparameters to adjust most of the pixels values to displaydomain and clap small part of the values to improve thecontrast. Comparisons with other image enhancementtechniques will be made.1. Single-scale retinex (SSR)The Single-scale retinex is given byRi( x,y)logIi(x,y)log[F(x,y)*Ii(x,y)]Where Ii(x,y) is image distribution in the ith color band,F(x,y) is the normalized surround function. F ( x,y)dxdy 1(2)The image distribution is the product of scenes reflectanceand illumination (3)Where Si(x,y) is the spatial distribution of illuminationand ri(x,y), the distribution of scene reflectances. Theconvolution with surround function works as averaging in theneighborhood. So thatGenerally the illumination has slow spatial variation, whichmeansThen it will be apparent that color constancy ( i.e.,independence from source illumination spectral and spatialvariation) is achievedVarious surround function could be used. Landproposed an inverse square spatial surround function,parameter,Si(x,y)ri(x,y)logSi(x,y)ri(x,y)i (4)Si( x,y) Si(x,y)Moore suggested the exponential formula with absoluteAnd Hurlbert studied the Gaussian,(5)ri(x,y)Ri(x,y) log(6)ri(x,y)2F( x,y) 1/ r(7)F( x,y)exp(r/ c)(8)2 2F(x,y) K exp( r/ c )(9)Kavitha Ramasamy

47 JEST-M, Vol.1, Issue 2, 20125. Comparison with other state of art image processingtechniquesIn order to analyze the advantages of the Retinex imageenhancement techniques, we compare the retinex result withother image enhancement techniques, including autogain/offset, gama correction, histogram equalization andhomomorphic filtering. As a result, all the other techniquesheavily depend on input images. For auto gain/offset, it couldachieve dynamic range compression but at the loss of detailsdue to saturation and clipping. For gama correction, it is goodto improve pictures either too dark or too bright but it is aglobal function applied to the picture, thus there is no detailsenhancement involved. For histogram equalization andhomomorphic filtering, they all failed for bi-modal pictures,which includes the information both dark and bright areas. (AsFig.8 but they all could achieve some better effect for Fig.9since the picture is in a general dark situation. But for retinex,it could achieve satisfactory results for both pictures, thus itsbenefits are obviously to see.IV. CONCLUSIONMultiscale retinex is an effective algorithm of retinex.It can have dynamic range compression and color renditioneffects at the same time. For color images, color restoration isnecessary in the case that the three-color channels are notintensity balanced. The gain/offset processes are used to shift,compress and clip the output of MSRCR to the displaydomain, which will improve the contrast. And one of theimportant characteristics of MSRCR with gain/offsetalgorithms is ―canonical‖, which means that the parameters ofnumber of scales, scales, gain and offsets do not vary fromimage to image or between band to band.As compared with other image enhancementtechniques, the Retinex enhancement has the followingadvantage: ―Canonical‖ constant – once it is defined,image enhancement is an automatic processindependent of inputs; Has general application on all pictures; Good dynamic range compression andcolor rendition effect.For the future work, the color image should befurther analyzed. Study the chapter 6 of the textbook ―DigitalImage Processing‖, try to analyze the color model suitable forthe retinex enhancement to improve the color correctionfunction (Ci(x,y)) to achieve the better color rendition, at thesame time, the empirical values of gain (G), offset (b) couldalso be improved.Modeling scene illumination is an important problemin computer vision. This claim is supported by the existenceof a large body of work addressing this problem. This workhas lead to improvements in image understanding, objectrecognition, image indexing, image reproduction, and imageenhancement. Nonetheless, much more work is required. Onemain problem is the development of algorithms for real imagedata. Most of the algorithms discussed above have quitespecific requirements for good results, and those requirementsare not met in most real images.Furthermore, even if the requirements are met, theyare not verifiable. Preliminary work suggests that the key toprogress is better overall models which include more of thephysical processes which impact the images. For example, bymodeling varying illumination, algorithms have beendeveloped which are not only robust with respect to varyingillumination, but can use the varying illumination for betterperformance.The same applies to specular reflection. Models forreal images must be comprehensive, because we cannotalways rely on the existence of certain clues such as varyingillumination or specularities. Furthermore, both these casesKavitha Ramasamy

48 JEST-M, Vol.1, Issue 2, 2012have connections to other computer vision problems such assegmentation and determining scene geometry from imagedata. Invariably, progress in these areas both aids modelingthe scene illumination, and is aided by modeling the sceneillumination. Thus there are great opportunities for progressusing more sophisticated and comprehensive physics basesmodels of the interaction of scene with illumination.Snapshots of ResultsThe Input Image.Enhanced Image using retinexScale=2W1=0.8,E2=0.2;C1=270,c2=10;Enhanced Image using retinexScale=1W1=1;C1=270;Enhanced Image using retinexScale=3W1=0.33,W2=0.33,W3=0.33c1=270,c2=150,c3=10Kavitha Ramasamy

49 JEST-M, Vol.1, Issue 2, 2012REFERENCES[1] J. A. Worthey and M. H. Brill, "Heuristic analysis of von Kries colorconstancy," Journal of The Optical Society of America A, pp. 1708-1712,3, 10, 1986.[2] L. B. Wolff, "A diffuse reflectance model for smooth dielectrics,"Journal of the Optical Society of America A, 11:11, pp. 2956-2968, 1994.[3] J. A. Worthey. "Limitations of color constancy," Journal of the OpticalSociety of America, [Suppl.] 2, pp. 1014-1026, 1985.[4] G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods,Quantitative Data and Formulas, 2nd edition, (Wiley, New York, 1982).[5] M. J. Swain and D. H. Ballard, "Color Indexing ," International Journalof Computer Vision, 7:1, pages 11-32 1991.[6] G. Sharma and H. J. Trussell, "Characterization of Scanner Sensitivity,"In IS&T and SID’s Color Imaging Conference: Transforms &Transportability of Color, pp. 103-107, 1993.Kavitha Ramasamy