Rational unsharp masking technique

Autor: Andrea Polesel, Giovanni Ramponi
Rok vydání: 1998
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Zdroj: Journal of Electronic Imaging. 7:333
ISSN: 1017-9909
DOI: 10.1117/1.482649
Popis: ARationalUnsharpMaskingTechniqueGiovanniRamponiandAndreaPoleselDEEI, University of Triestevia A. Valerio 10,I-34127Trieste,Italye-mail:ramp oni@univ.trieste.itFinal version Nov.1997, accepted for publication in theJournal of Electronic ImagingAbstractThelinearUnsharpMaskingtechniqueusedinimagecontrast enhancementis mo di ed in this pap er,by intro-ducing acontrol term expressedasarational function ofthelo calinputdata.Inthisway,noiseampli cation isavoided and, at the same time, oersho ot e ectson sharpedges are limited. Exp erimental results supp ort the valid-ity of the metho d, even as a prepro cessor for interp olationsystems.Keywords:Image enhancement, Unsharp Masking, Ra-tional lters, Nonlinear op erators.I.IntroductionThe technique of Unsharp Masking (UM) was intro ducedin photography to improve the quality of pictures by mak-ing their details crisp er; it consisted in optically subtractinga blurred copy of an image from the image itself.Its digi-tal version, due to its simplicity and relative e ectiveness,has b ecome a to ol of widespread use in the image pro cess-ing community, describ ed in any textb o ok [1] and includedin many available softwarepackages (e.g.,[2]).Indigitalimage manipulation, it can b erealized as shown in Fig.1,by pro cessing the image with an highpass lter (usually aLaplacian), multiplying the result by a scaling factor, andadding it to the original data.Notwithstanding its p opularity, this technique su ers fromtwo drawbacks which can signi cantly reduceits b ene ts:noise sensitivity and excessiveoversho ot on sharp details.TheformerproblemcomesfromthefactthatUMmetho dassignsanemphasistothehighfrequencycom-p onents of the input, amplifying a part of the sp ectrum inwhich the SNR is usually low.On the opp osite, wide andabrupt luminance transitions in the input image can pro-duce oversho ot e ects; these are put into further evidenceby the human visual system through the Mach band e ect[1].Various mo di cations have b een intro duced in the basicUM technique, in particular to reduce the noise ampli ca-tion problem.A quite trivial approach consists in substi-tuting a bandpass lter for the highpass one in Fig.1. Thisof course reduces noise e ects, but also precludes e ectivedetail enhancement in most images. In more sophisticatedapproaches,nonlinearop erators(orderstatistics,p olyno-mial, logarithmic) are used to generate the correction signalwhichisaddedtotheimage; wewill citeafewof them,without attempting a thorough overview of the eld.Amo di edLaplacian lter,calledtheorderstatistics(OS)Laplacian,isprop osedin[3];itsoutputprop or-tional to the di erence b etween the average and the medianof the pixels in a window. The resulting OS-UM algorithmis evaluated for its p erformance on a convex/concae edgemo del and on white Gaussian noise input signal, showingits robustness and its enhancing characteristics.Alternatively, a p olynomial approach can b e used.In [4],the Laplacian lter is replaced by a simple op erator basedon a generalization of the so-called Teager's algorithm. Un-der reasonable hyp otheses, this op erator approximates theb ehaviour of a lo cal-mean-weighted highpass lter, havingreduced high-frequency gain in dark image areas.Accord-ingtoWeb er'slaw[1],thesensitivityofhumanvi-sualsystemishigherindarkareas;hencetheprop osed lter intro duces a p erceptually smaller noise ampli cation,without diminishing theedge{enhancing capability of theUM metho d.Another p olynomial metho d, the Cubic UM(CUM)technique,hasb eendevised[5]:its purp oseis toamplify onlylo calluminancechangesduetotrueimagedetails.ThisisachievedbymultiplyingtheoutputoftheLaplacian lterbyacontrolsignalobtainedfromquadratic edge sensor.Still in the p olynomial framework,a class of quadratic lters is de ned in [6], where the lterco e cients at a given p osition in the image are calculatedbytakingintoaccountthegreyleveldistributionofsurrounding pixels.A Gaussian or an exp onential functionis used to reduce the contribution of pixel values the lumi-nance of which is di erent from that of the center pixel.Finally, to acquirea b ettercontrol on therangeof theimage brightness,theUMmetho d canb ecoupledto ho-momorphic ltering [1]:in this case, the image is rst con-vertedto thelogarithmic domain, thenUMis p erformedand the output is exp onentiated and scaled [7].To try and cop e with b oth the drawbacks indicated ab ove,i.e.noiseampli cation and excessiveoversho ots,alinearadaptive op erator is prop osed in [8]. The LMS technique isused to change the value of the scaling factor ( in Fig.1)at each lo cation of the image; to this purp ose, the pixel tob e pro cessedis lab elled as b elonging to a smo oth area orto a medium{ or high{contrast area.Go o d quality resultscan b eachieved,at theexp enseof a relatively high com-putational complexity. In this pap er we attempt to obtaina similar result with a simpler, non{adaptive metho d.ThebasicUMschemeisstillusedhere,butarationalfunc-1
Databáze: OpenAIRE
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