Contrast limited Adaptive Histogram Equalization and Discrete Wavelet Transform Method Used for Image Enhancement

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1 2017 IJRI Vlum 2, Issu 8 ISSN: Cntrast limitd Adaptiv Histgram qualizatin and Discrt Wavlt ransfrm Mthd Usd fr Imag nhancmnt 1 Smitha N, 2 Ujwala B S, 3 Dr. Manjunatha P, 4 Chthan L S 1 M.ch. (DCS), PG schlar, Dpt. f C, JNNC, Shimga, Karnataka, India 2 Assistant Prfssr, Dpt. f C, JNNC, Shimga, Karnataka, India 3 Prfssr, HOD f C, JNNC, Shimga, Karnataka, India 4 Assistant Prfssr, Dpt. f CS, PSIM, Shimga, Karnataka, India Abstract Imag nhancmnt has fund t b n f th mst imprtant visin applicatins bcaus it has ability t nhanc th visibility f imags. Cntrast limitd adaptiv histgram qualisatin (CLAH) is a gd cntrast nhanc algrithm, but it facs vr strtching and nis prblms. slv ths prblms, a nw cntrast nhancmnt mthd is implmntd which is namd as Cntrast limitd adaptiv histgram qualisatin (CLAH)-discrt wavlt transfrm (DW), ths tchniqu cmbins tw mthdlgis DW and CLAH. his nw mthd implmntd in thr main stp: First, using DW dcmpss riginal imag int lw frquncy and high frquncy cmpnnts. hn apply CLAH t lw-frquncy cfficints and t cntrl nis nhancmnt, high frquncy cfficints ar kpt unchangd. his is du t high frquncy cmpnnts which hav all infrmatin abut th imag and als cntains niss f riginal imag. Finally, using invrs DW rcnstructd th imag by taking nw cfficints. liminat vr nhancmnt, wighting avrag f rcnstructd imag and riginal imag is calculatd using wighting factr matrix. h wighting pratin is dn t cntrl th nhancmnt lvl f rgin alng with diffrnt luminancs in riginal imag. his is mst imprtant bcaus bright parts f imag ar usually unncssary t b nhancd in cmparisn with th dark parts. Hnc ths implmntatin shws that this mthd prfrms wll t supprss nis and t cntrl vr nhancmnt. Finally th nhancd imag btaind using CLAH-DW fr chcking th quality f nhancd imag, Pak Signal t Nis Rati (PSNR),MA,LI,N is calculatd and ths simulatins ar dn using MALAB Indxrms Histgram qualizatin, Cntrast limitd adaptiv histgram qualisatin (CLAH), Discrt wavlt transfrm (DW), Lcal ntrpy incrmnt (LI). I. INRODUCION Imag nhancmnt play a vry imprtant rl in imag prcssing whr ppl will slct th imag with rspct t imag infrmatin. Imag nhancmnt frms which cmpris nis rductin, sid nhancmnt and als distinctin nhancmnt. nhancmnt is n f th bttr ubiquity tchnlgy f a lctrically savd imag. mak imag lightr r darkr r t dvlp r slash cntrast. Imag nhancmnt which givs a strngth t th snsitivity f infrmatin in imags fr human visin,r t prsnt nhancmnt input imag fr thr imag prcssing prcdur. h cntrast nhancmnt n f th significant kind f prcssing tchnlgy fr imags and als fr vids. It can ffctivly imprvs th visual quality f an imag fr human rcgnitin and prcptin. Alng with this, prprcssing mthd is imprtant fr autmatic pattrn rcgnitin and als fr machin vid, t gt ssntial faturs in vids and imags and als fr thr applicatins. imprv th cntrast f an imag many cntrast nhancmnt tchnlgy has bn intrducd. hs cntrast nhancmnt prcdur can b dividd int tw catgris 1) Spatial dmain basd mthd 2) ransfrmatin dmain basd mthd Spatial mthd rfr t imag plan itslf, and this apprachs ar basd n dirct manipulatins f picturs in an imag.ransfrmatin mthd ar basd n th furir transfrm mdificatin f an imag. In imag nhancmnt Histgram qualizatin is n f th basic mthd[2]. In this mthd cntrast adjustmnt can b dn using imag histgram in imag prcssing. By applying a gray lvl transfrm th glbal cntrast f an imag incras and als which maks rsulting histgram flattn. Histgram qualizatin which applis bst n vr r undr xpsd imag, which hav narrw cntrast rang. Sinc th Histgram qualizatin is applid n th ntir imag, th lcal dtails ar nt nhancd adquatly. rduc ths drawbacks, lcal histgram qualizatin basd tchniqus ar prpsd. On f th lcal histgram qualizatin basd imag nhancmnt mthd is cntrast limit adaptiv histgram qualizatin (CLAH)[3]. In CLAH mthd which clip th histgram abv th clip limit and als distributd t th sm thr histgram f varis rgins which will hav histgram valu blw th clip limit. But this mthd facs nis nhancmnt prblm and cntrast vrstrtching prblms. vrcm this prblm a nw mthd is dfind,which is a nvl imag nhancmnt tchniqu, namd as cntrast limit adaptiv histgram qualizatin discrt wavlt transfrm(clah-dw). CLAH-DW tchniqu is als n f th cntrast nhancmnt mthd which uss bth DW and CLAH. his mthd uss DW first which divids th input imag int lw frquncy cfficint and high frquncy cfficint. hs tw cmpnnt rfr t apprximatin and dtail infrmatin f riginal imag, rspctivly. In this high frquncy cmpnnt mst f th nis is IJRI Intrnatinal Jurnal fr Rsarch rnds and Innvatin ( 142

2 2017 IJRI Vlum 2, Issu 8 ISSN: prsnt hnc kpt this cmpnnt unchangd and nly lw frquncy cmpnnt nhancd using CLAH which rducs th nhancmnt f nis viably. Finally, by using invrs DW rcnstructd imag frm nw cfficint and als calculat th wightd sum f riginal imag. h wighting cfficint is prprtinal t th intnsity f riginal imag. his maks th rgin with varius luminancs nhancd suitably and in this mannr ass vr-nhancmnt[1]. II. PROPOSD HAS MHOD In this mthd usd t rmv vr nhancmnt r nis rmval in imag systm. Fr nhancmnt prcss w us bth cntrast limitd adaptiv histgram qualizatin - discrt wavlt transfrm. mak it mr undrstandabl, w thn furthr xplain th DW and wighting pratin in ur prpsd mthd. h prcdurs f th CLAH-DW algrithm ar givn as fllws: Stp 1: Dcmps th riginal imag int lw-frquncy and high-frquncy cmpnnts by N-lvl DW using Haar wavlt. h Haar wavlt is simpl and thus suitabl fr hardwar implmntatin. h chic f paramtr N is discussd in dtail latr. Stp2: nhanc th lw-frquncy cfficints using CLAH and kp th high-frquncy cfficints unchangd. Stp 3: Rcnstruct th imag by invrs DW f th nw cfficints. Stp4: Finally, tak th wightd avrag f th rcnstructd and riginal imags using h riginally prpsd wighting cfficint maks th rgins with diffrnt luminancs nhancd apprpriatly and thus allviats vr-nhancmnt ffctivly[1]. Fig.1: Ovrall flw f th prpsd CLAH-DW basd imag nhancmnt mthd. 2.1 xplanatin abut DW Using DW dcmps th input imag int fur sub band imags, which can b dfind as Lw-Lw(LL), Lw-High (LH), High- Lw (HL), and High-High (HH).Sub bands with its frquncy cmpnnts cvr th full frquncy spctrum f th riginal imag. hrtically, in rdr t gnrat diffrnt sub band frquncy imags,a filtr bank shuld b pratd n that imag. Fr stimating dgs in highr frquncy sub bands, mdl is prpard using dgs idntifid in lwr frquncy sub bands and nly th cfficints with significant valus ar cnsidrd as th vlutin f th wavlt cfficints[5] xplanatin abut CLAH In sm cass, whn grayscal distributin is lcalizd, it may nt b tmptd t transfrm lw cntrast imags by Histgram qualizatin apprach. Hnc, in ths cass adjusting th curv may includ fragmnts with high slp implis tw grayscal may b mappd t fundamntally uniqu grayscals. his issu can b slvd by limiting th cntrast using Histgram qualizatin and th stratgy utilizd fr this cnditin is knwn as CLAH (Cntrast Limitd Adaptiv Histgram qualizatin). hugh n applying AH[4], th nis gt mr nhancd in th rgin whr it has small intnsity but thr might b a fw rlics n that rgin. CLAH is th rasnabl tchniqu that is utilizd t cnfin ths ld rlics. 1) Gt all th inputs: Imag, Numbr f rw and clumn dirctins f rgin, Numbr f bins usd in frm imag transfrm functin fr th histgrams, Clip limit takn nrmally frm 0 t 1 fr cntrast limiting. 2) Pr-prcss th inputs: if ncssary find th ral clip limit frm th nrmalizd valu, bfr splitting imag int it rgin pad th imag. 3) Prcss ach tils thus frms gray lvl mappings: ak a singl imag rgin, utilizing th spcifid numbr f bins maks a histgram fr this rgin, clip th histgram using clip limit and als crat and crat a mapping(transfrmatin functin) fr this rgin. 4) Intrplat gray lvl mappings in rdr t frm final CLAH imag: tak clustr f 4 nighbring mapping functins, prcss imag rgin which is partly vrlapping ach f th mapping tils, xtract a singl pixl, apply fur mappings t that pixl, and intrplat btwn th rsults t btain th utput pixl; rpat vr th ntir imag. IJRI Intrnatinal Jurnal fr Rsarch rnds and Innvatin ( 143

3 2017 IJRI Vlum 2, Issu 8 ISSN: xplanatin abut Luminanc cmpnnt valu V f imag: h nw CLAH-DW mthd can b asily xtndd t nhanc clr imags by applying it n th luminanc cmpnnt f imag. h luminanc cmpnnt valu f imag can b calculatd using th fllwing quatin max(r in, G in,b in ) = 255 (1) Whr ( Rin, G in,b in )ar th RGB valus f input imag. In rdr t prvnt clr distrtin, th final nhancd clr imag is btaind using th fllwing pratin Rut Rin, ut in G G B in, ut in Bin (2) in Whr ( R,, ut Gut B ut )ar th RGB valus f utput imag and is th nhancd vrsin f using CLAH-DW xplanatin abut Wighting avrag: mitigat vr nhancmnt, w tak th wightd avrag f th rcnstructd and als riginal imag as fllws I I K I ( Matrix K) (3) r n whr dnts a pint-t-pint multiplicatin pratin. I, I, I r ar, rspctivly, th riginal, rcnstructd and final nhancd imags. is brightnss cmpnsatin factr, which is usd t cmpnsat th dcrasd luminanc f imag bcaus f th wighting pratin. In ur mthd, th valu f α (1< α <2) is chsn mpirically accrding t th luminanc f riginal imag. Matrix n is a matrix f all ns. K and Matrix n K ar th wighting cfficints f I and I rspctivly. h wighting factr matrix K is dfind in th fllwing way K(1,1) K(1, n) K K( m,1) K( m, n) m n f ( I(1,1)) f ( I(1,1)) f ( I( m,1)) f ( I( m, n)) mn (4) h siz f bth I and K ar m n I( p, q) ( p 1,2,... m, q 1,2,... n is ) th gryscal f pixl (p, q ) in th riginal imag. I,β is a rgulatry xpnnt. h functin f is dfind as fllws I( p, q) I f ( I( p, q)) I I I max and I min dnt th maximum and minimum gry scals f riginal imag, rspctivly. As w can s frm (3),( 4) and (5) imag is nhancd mr using CLAH-DW with a gratr rgulatry factr β. Whn β=0 w hav I I.In th thr xtrm cas whn β=+ w hav I I.Hnc, CLAH-DW may unabl t nhanc th imag sufficintly whn β is t small and it may fac vr nhancmnt prblm whn β is t big. Lcal ntrpy incrmnt (LI) t calculat th ptimal β valu. max min min m1 m2 1 ntrp ( ) LI ( ) 20ln m m ntrp 1 2 l1 k1 (5) (6) IJRI Intrnatinal Jurnal fr Rsarch rnds and Innvatin ( 144

4 2017 IJRI Vlum 2, Issu 8 ISSN: ntrp p( m)lg( p( m)) (7) m0 whr m 1 and m 2 ar th rw numbr and clumn numbr f blcks ntrp and ntrp ( ) ar th ntrpy f riginal imag and CLAHW-DW imag nhancd imag with rgulatry factr β in a givn blck and p(m) is th prbability f th m th = (0, 1, 2,...255)gry lvl. h ptimal valu f á in [0, 5] can b btaind by th fllwing quatin arg max{ LI ( )} (8) [0,5] In ur xtnsiv xprimnts, th paramtrs β btaind using (6) prducs gd subjctiv quality in mst cass. III. XPRIMNS RSULS On this sctin, w xamin ur mthd with Histgram qualizatin (H) qualitativly and quantitativly and ntic th ffcts f paramtrs invlvd. h pratin n H is cnvrting th RGB clrspac int HSV aftr which cnvrting back t th RGB clrspac. h pratin xcutd n th V channl f th HSV clrspac. Instantly manipulating OF ach f vry RGB channl lads t visually incnsistnt with th actual imags. In trms f visual quality and tim cst ur mthd is mst significant. Fig. 2a: First rw is inputs i. imag1,imag2,imag3,imag4 Fig. 2b: Scnd rw rprsntd CLAH utput Fig. 3c: hird rw is wighting avrag CLAH-DW utput Figur1 shws th ach stp f CLAH-DW.CLAH-DW nly nhancs th lw-frquncy cmpnnt and kps th highfrquncy cmpnnt which cntains mst f th nis in riginal imag unchangd. It als can b bsrvd that sm dtails in th bright parts f nhancd imags using CLAH ar lst bcaus f vr nhancmnt. h vr nhancmnt phnmnn is allviatd in ur mthd. cmpar ths imag nhancmnt mthds quantitativly, fur bjctiv valuatin indxs, that is, LI, nis stimatin (N), pak signal t nis rati (PSNR) and man abslut rrr (MA) ar usd.h LI is adptd t masur th cntnt f an imag and a highr valu indicats th imag with richr dtails. Bth N and PSNR ar adptd t quantify th artfacts r nis gnratd during cntrast nhancmnt prcss. MA is th abslut diffrnc btwn th input and utput man intnsiti abl 1: Varius input imag and its paramtr Input MA PSNR N LI Imag Imag Imag Imag IJRI Intrnatinal Jurnal fr Rsarch rnds and Innvatin ( 145

5 2017 IJRI Vlum 2, Issu 8 ISSN: It is difficult t cnstruct such datasts, fr th sak f bjctivnss, w hav t chs a rliabl a datasts. w als adpt th rsults f fur paramtr rsult as rfrnc. abl shws a MA,N,PSNR,LI f diffrnt imags cmpard with th H. Figur2 Frm that, w bsrv ur mthd is significantly utprfrm th thrs. h rsults btaind by CLAH-DW ar mr ppular and clsr t th rfrncs than th thrs. IV. CONCLUSION Imag nhancmnt is n f th mthd fr imprvmnt f imag apparanc by incrasing sm dminanc faturs r by dcrasing a ambiguity btwn diffrnt rgins f th imag. Imag nhancmnt prcsss which hav many numbr nhancmnt tchniqu which imprvs visual quality f a imag r cnvrt imag int thr bttr frm which suitd fr th analysis f human r machin. Many applicatin which uss imag nhancmnt mthd but imags suffr frm pr cntrast,hnc it is ncssary t nhanc th cntrast. On f th bttr cntrast nhancmnt is CLAH-DW which cmbins tw mthdlgis DW and CLAH. In this mthd dcmps th imag int lw frquncy and high frquncy cmpnnt using DW.ak nly lw frquncy fr furthr nhancmnt. Bcaus lw frquncy cmpnnt having apprximatin dtails f imag and high frquncy which having mr nis and als dtail infrmatin abut imag. Hnc high frquncy cmpnnt kpt unchangd. Finally find th wighting sum f rcnstructd imag and riginal imag t liminat vr nhancmnt by using wighting factr matrix. his mthd which givs gd quality f nhancd imag than th thr nhancmnt mthd. In this prjct imag nhancmnt is implmntd with th varius mthd and cmparisns ar prfrmd by cnsidring MS, PSNR, N,MA,LI t idntify quality f nhancd imag. V. ACKNOWLDGMN W d lik t mntin ur sincr thanks t Mrs.Ujwala B.S, Asst Prfssr, C dpartmnt, JNNC, Shimga and Mr. Chthan L S, Asst Prfssr f CS dpartmnt, PSIM, Shimga fr thir cntinuus supprt and valuabl suggstin during th implmntatin f th prpsd systm. RFRNCS [1] Huang Lidng, Zha Wi. Cmbinatin f cntrast limitd adaptiv histgram qualisatin and discrt wavlt transfrm 88 fr imag nhancmnt. I Imag Prcss, Vl. 9: , [2] Arici, Dikbas, S, Altunbasak, Y. A histgram 0mdificatin framwrk and its applicatin fr imag cntrast nhancmnt. I RANSACIONS, Vl. 9: ,2009 [3] Huang S.C, Chng F.C, fficint cntrast nhancmnt0using adaptiv gamma crrctin with wighting distributin, 0000 I rans, 2013, 22, (4), pp [4] Alx0Stark. J, Adaptiv imag cntrast nhancmnt using gnralizatins f histgram qualizatin, Cmput. Vis. Graph. Imag Prcss., 1987, 39, (3),pp [5] Fattal, R, dg-aviding wavlts and thir applicatins, ACM ransactins n Graphics, 2009, p.22. IJRI Intrnatinal Jurnal fr Rsarch rnds and Innvatin ( 146