a new wavelet-based method for contrastledge enhancement

8/18/2019 A New Wavelet-based Method for Contrastledge Enhancement

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A NEW WAVELET-BASED METHOD FOR CONTRASTlEDGE ENHANCEMENT

Jinhui Qin and Muhmoud R El-Sukku

Computer Science Department, Universityof Western OntarioLondon, Ontario, Canada

fjhqin, elsakka}@csd.uwo.ca

ABSTRACTContrast enhancement is usually achieved by histogramequalizing image pixel gray-levels in the spatial domain toredistribute them uniformly. Meanwhile, edge enhancementattempts to emphasize the fine details in the original image. Butin the spatial domain itis hard to selectively en hance details atdifferent scales. Moreover, in the spatial domain, applyingcontrast and edge enhancement techniques in different ordersmay yield different enhancement results. To overcome theabove spatial domain enhancement issues,a new wavelet-basedimage enhanc emen t method is proposed. The proposed methodhistogram-equalizes theapproximation-coefficiend. t the sametime, it high-boost filters the detail-coefficients at selectedresolution levels separately. The experiments show that utilizingthe proposed method can achieve robust contrast and edgeenhancement. Moreover, the computation cost in the waveletdomain is less than that in the spatial domain. This is especiallyb-ue when considering that currently most images are alreadywavelet-compressed (the current JPEG 2000 standard is awavelet based scheme).

1. INTRODUCTIONIn this research we focus on contrast and edge enhancementissues [1][2]. Contrast enhancement, in general,is achieved byutilizing the entire brightness range ina given image. Thehistogram of an image usually provides a way to determinewhich particular gray scale transformation is required toenhance the image contrast. Histogram equalization (HEQ)isone of th c most useful contrast enhancement schemes. When animage's histogram is equalized, image pixel values are mappedto uniformly distributed pixel values, as muchas possible.However, afier applying the HEQ technique toan image in thespatial domain, multiple gray-levels in the image could bemerged into o ne level. This resultsi n a loss of small d etails afierperforming the HEQ procedure.[3][4].Edge enhancement attempts to emphasize edges, or details,i n agiven image. Mask convolution isa commonly used techniquefor enhancing edges[2][5]. But in the spatial domain, itis hardto selec tively enhanc e details at different scales.Moreover, in the spatial domain, applying contrast and edgeenhancement techniques in different orders may yield differentenhancement results.Ideally, if we can decompose an image into several compon entsin multiple resolution levels, where low-pass and high-passinformation are kept separately, then we can enhance the imagecontrast without disturbing any details. At the same time, wecan also emphasize image detail, ata desired resolution level,without disturbing the restof the image information. Finally, byadding the enhanced comp onents together, we should geta more

impressive result. Note that, in this case the enhancement orderis irrelevant, since we are dealing with separate components.The wavelet transform framework provides an opportunity toachieve these tasks. It provides multiple resolutionrepresentations of a given image, each of which highlightsscale-specific image featu res [6][7]. Sinc e featu resin thosewavelet-transformed components remain localized in space,many spatial domain image enhancement techniques can beadopted for the wavelet domain.

n this research, a new wavelet-based image enhancementmethod is proposed [SI. The proposed enhancement methodconsists of two parts. One part is to enhance the image contrast.The contrast enhancementis achieved by histogram equalizingthe wavelet approximation-co~~cienis which arecorresponding to the low-pass information of a given image.The other part is to enhance the image details ata desiredresolution level. This enhancementis achieved by high-boostfiltering (HBF) [2] the waveletdetail-coeflcients, which arecorresponding to the detail information ofa given image.

2 LITERATURE REVIEWSn literature, there are many attempts lo enhance images in the

wavelet domain, rather than in the spatial domain, to benefitfrom the multiple-resolution nature of the wavelet domain.Chen et al. [9] lobally shiRed the intensity value in theapproximation-coefficients to achieve contrast enhancement.

But they didn't provide an efficient way to dec ide the size of theshifiing step. They also established a zero-crossing tree, whichconsists of zero-crossings of each companent in multiple-resolution levels, to rep res en t multiple-resolution e dges that areused to suppress noise.Fu et al. [3] analyzed the drawbackof the HEQ procedure in thespatial domain. Then they proposeda wavelet-based contrastenhancement method. In their method, afier performing theHEQ procedure in the spatial domain, the output image wastransformed into the wavelet domain. Thenall approximation-coefficients were squared. They claimed that the proposedprocess could compensate for the information that was lostduring the HEQ process.Reeves et al. [41 investigated a wavelet transform domain filter,based on the LLM MSE filter[ I O] to suppress noise and enhanceedges. They also applied global HEQ to the wavelet

approximation-coefficients at the coarsest decomposition levelto enhance contrast. But further investigation was required inorder to understanding how the selection o f the approximation-coefficients' range and histogram bin values affects thereconstructed image.Xu et al. [ I ] used a wavelet phase filter [I21 at finer scales inthe wavelet domain to reduce noise, anda semi-sotl wavelet

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shrinkage technique [ I 3 at coarse scales in the wavelet domainto further reduce noise. But the proposed method still could notautomatically adjust its parameters to achieve optimal result.There are some other image enhancement attempts. Forexample, Gong et al. [I41 rationally enlarged coefficients onmultiple scales in the wavelet domain. Xu et al. [ I51 altered theamplitude o f coefficients in the wavelet domain. And Peng e t al.[I6 used a non-linear enhancement operator on coefficients atmulti-scale in the wavelet domain. However. these attemptsaimed at improving only image contrast.

3. PROPOSED WORKThe proposed enhancement method [a] consists of two parts.One part i s to enhance the image contrast, which i s achieved byhistogram equalizing the wavelet approximation-coefficients.The other part i s to enhance the image details at a desiredresolution level, which is achieved by high-boost filtering thewavelet detail-coefficients. Fig. I shows the framework of theproposed scheme.It is worth mentioning that the implementation of the HEQ

method in the wavelet domain is a litt le hit different from that inthe spatial domain. This i s because wavelet transformedcoefficients are floating point numbers. Moreover, some of themare possibly negative. Choosing an appropriate range ofcoefficient values and setting of bins thresholds for HEQ in thewavelet domain i s not an easy task.We suggest a new way to implement HEQ in the waveletdomain. First, all coeficients are casted into integer values. Thehistogram is then built based on those integer values.just like inthe spatial domain. Second, to equalize this wavelet domainhistogram, a procedure which is simillar to the spatial domainHEQ is used. Finally, after calculating the difference of eachcasted integer value before and aAer equalization, the finalmapped value of each original coefficient is obtained by addingthe difference to each original coefficient.Note that, i f the original image does not use most of theavailable dynamic range, the transformed coefficients will notuse most o f heir dynamic range either. Therefore the coefficientvalue range over which the HEQ is performed in the waveletdoamin could he expanded beyond the current range to achievea better contrast enhancement result. But the expanded rangeshould not exceed the boundary values of this component,otherwise, some artifacts may he introduced to the resultingimage.

4. EXPERIMENT RESULTSIn the proposed method, there are various parameters in each ofthe three major functions (wavelet transform, HEQ and HBF inthe wavelet domain functions) that need to he adjusted.Different parameter selections will affect the enhancementresult. The final parameter selections are based on the followingresearch results.

4.1. Experimental setupIn this research, the proposed method is tested on many images,which have various characteristics. In order to accommodate hesize constraints of this paper, the Lenna image has been chosento demonstrate our results. The Lenna image contains a nice

mixture of detail, flat regions, shading, and texture that do agood job of esting various imageenhancement algorithms.

4.2. Wavelet Transform FunctionThe purpose of this function is to perform a forwardinverse

wavelet transform on a given image. After applying a forwardwavelet transform, the given image is decomposed into severalcomponents i o multi-resolution. Note that, using differentwavelet filter Sets andlor different number o f transform-levelswi ll result in differenl decomposition results.While any wavelet-filters can he used with the proposedscheme. we restricted our experimentation to the one-level Haarwavelet-filter, to limi t the scope o f this research and to focus onthe proposed method only. We le ft testing the proposed methodon various wavelet filter sets and multiple wavelet levels as afuture work.

4.3. HEQ FunctionWhen applying HEQ in the spatial domain, some small detailsmay b e eliminated after HEQ. A I D signal example has beengiven in Fig2 to show the lost information after applying the

HEQ procedure in the spatial domain. For example, comparesignal A (the original I D signal) and signal B (the signal A afterHEQ in the spatial domain) at location I O . In the waveletdomain, the dztail-information and approximation informationare kept separately as shown in signal C and D, respectively.Note that applying HEQ on the approximation-coefficients Donly, to get E, does not disturb any details in C. Therefore. thereconstructed signal F, from E and C, preserves all details insignal A. Hence, performing HEQ in the wavelet domain canprevent detail information from being lost.The purpose of contrast enhancement is to redistribute pixelvalues uniformly, as much as possible. Fig.3 ig.8 show theLena image and i t s histogram hefore and after performing HEQin the spatial and the wavelet domains. We can obseNe that:

The original image in Fig.3 does not utilize the entiredynamic range of pixe l values and the pixel values are not

uniformly distributed.The image in Fig.4 utilizes the ent i re dynamic range.However there are some gray-levels have been eliminated.

The image in Fig.5 util izes the entire dynamic range ofpixel values. Moreover, pixe l values are uniformly utilizedover the entire dynamic range. From this observation (andthe other runs that are performed on other images, but arenot included in this paper), we can conclude that theproposed wavelet-based HEQ method outperforms thespatial domain HEQ method.

4.4. H BF FunctionThe purpose of HBF i s lo enhance high frequency informationof a given image. The proposed method treats each detailcoefficient hand as a two-dimensional image (called image-hand) and applies HBF on these image-hands in the waveletdomain (by subtracting the low-pass filtered image-hand fromthe scaled original image-hand, where the scaling factorA i s notl e s s than I):

Highboost = ( A ) Original LowpassThe factor A determines the degree of high frequency emphasis.During the course of this research, a 7 x 7 Gaussian filter with astandard deviation equal 1.0 is used as the low-pass filter in the

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N levels inverseWaYelet trmsfomDetail-coefficients

..........................::p,064 .. ..

0 I O 20 30 40

ig1 . Framework of the proposed method

........ .........................-D ...... ......

A ..................20 O 20 30 40 '0 10 20 30 40

Fig.2. A: the original ID signal, B: the signal A after HEQ in the spatial domain,C: the detailed-coefficients o f A in the Haar wavelet

domain, D: the approximated-coefficientsof A in the Haar wavelet domain. E: the approximated-coefficients of A after HEQ in theHaar wavelet domain, F: the original signal A,after performing HEQin the wavelet domain (reconstructed from C andE).

Fig.3. Original Lena image Fig.4. After HEQ in the spatial domain Fig.5. After HEQin the wavelet domain

I8ocm 300

2 a -

loo(. 100 600

200 2oc

50 Io0 150 200 2 0 50 100 150 200 250 0 50 100 150 200 250

Fig.6. Histogramof he image in Fig.3. Fig.7. Histogramof the image in Fig 4. Fig.8. Histogramof the image in Fig.5.

HBF function. The influence of the factor is observed throughexperiments As the value is increased, a stronger edgeemphasized result is achieved. However, we should know thatcoefficients of each component have upper and lower bounds.This means that if the A value is increased beyonda certain

value, the enhanced pixel values may jump outside the allowedboundary range. n this case, these coefficient values will becast lo the nearby boundary (a saturation issue) The castingprocess may introduce artifacts.

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Fig.9. Original Lena image Fig.10. ‘The image aRer HEQin thespatial domain

Fig. I The image after HEQ andHBF in the wavelet domain

4.5. Result of using the proposed method [5] Rosenfeld and A Kak, ita/ Picture Processing, 2”dFig.9 shows the original Lena image. Fig.10 shows the result ed., Academic Press. New York, 1982.after using HEQ in the spatial domain. The resultof using the [6] C. BUR US,R. Gopinath and H. Guo, Introduction toproposed method (i.e., using HEQ and HBF in the wavelet Wovelets and Wavelet Transformations: A Primer,doma in) is shown in Fie.1I Prentice-Hall. New Jersev. 1998.

3 CONCLUSIONSIn this paper, a new wavelet-based method for contrastledgeenhancement is proposed. Experimental results show that thehistogram of a given image after performing the histogramequalization procedure in the wavelet domainis redistributedmore uniformly than that after performing the histogramequalization proced ure in the spatial doma in.Since contrast and edge enhancement procedures are appliedseparately in the wavelet domain, they do not affect each other,and the order o f applying bothof them becomes irrelevant.Moreover, in the spatial domain, the enhancement process isperformed on the full size of an image. While in the wavelet

domain, the enhancement process is performedon

someof

thedecomposed components, each of which is at most a quarter thesize of the original image. Therefore, computation costin thewavelet domain is less than thatin the spatial domain. This isespecially true when consid ering that currently most images arealready wavelet-compressed (the current JPEG2000 standard isa wavelet based scheme).

6 ACKNOWLEDGEMENTThis work was partially support by the National Science andEngineering Council (NSERC) of Canada. This support isgreatly appreciated.

[ I ]7. REFERENCES

R. Gonzalez, ‘ nage Enhancement and Restoration”,Handbook o Parrern Recognition and Image P rocessing,Academic Press,New York, pp. 191-213, 1986.R. Gonzalez and R Woods, Digifal Image Processing,Addison-Wesley Publishing, 1992.J Fu, H. Lien and S Wong, “Wavelet-based HEQ ofgastric sonogram images”, Computerized MedicalImaging and Graphics, Vo1.24; pp. 5p-68 2000.T. Reeves and M. Jemigan , “Multiscale-based Imageenhancement”. Canadian Conference on E/ectrica/ andCompu ter Engineering. Vol 2. pp.50&505, 1997.

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..E. Aboufadel and S Schlicker, Discovering Wavelets,John Wiley Sons New York. 1999.Jinhui Pin, “A new wavelet-based method forcontrastledge enhancement”, M Sc Dissertation.Computer Science, University of Western Ontario,London. Ontario, Canad a, 2001.L. Chen, C. Chen and K. Parker, “Adaptive featureenhancement form mammographic images with waveletmulti-resolution analysis”, J a u r m l o ElectronicImaging, Vol.6, No. 4, pp. 46 74 78 , October 1997.D. Kuan. A. Sawchuk, T. Strand and P. Chavel,“Adaptive noise smoothing filter for images with signal-dependent noise”, IEEE Transactions on Pattern

Analysis and Machine Intelligence, Vo l. PAMI-7, No. 2,pp. 165-177, March 1985.K. Xu, X. Zheng and X . Cheng, “A novel method forimage enhancement of medical images based onwavelet“, Acta Electronica Sinica, Vol. 27, No. 9_ pp.121-123 1999.E. Olsen and B. Lin, “A w avelet phase filter for emissiontomography”, The Proceedings o the SPIE, Vol. 2491,pp. 82 94 39 , 1995.A . Bruce and I I Gao, “Waveshrink Shrinkage functionand thresholds”, The Proceedings ofthe SPIE,Vol. 2569.PD. 27&281, 1996..W. Gong and Y. Wang, “Contrast enhancement ofinfrared image via wavelet transform“, Journal oNational Universiry ofDefense Technology. Vo l. 22, No.6 pp. I 17-1 19,2000.

B. Xu, C. Fu and J. Ma, “Image enhancement methodbased on wavelet transform”, Proceedings of rhe SPIE.Vol. 4044, pp. 150-157 2000.B. Peng W. Fu and C. Yang, “Contrast enhanc emen to f radiographs using shiR invariant wavelet transform”.Wuhan University Journal o Natural Sciences, Vo1.5,No.1 , P59-P62, 2000.

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a new wavelet-based method for contrastledge enhancement

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