A NOVEL APPROACH TO QUALITY ENHANCEMENT OF GRAYSCALE IMAGE USING PARTICLE SWARM OPTIMIZATION

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1 ISSN: (Onlne) A NOVEL APPROACH TO QUALITY ENHANCEMENT OF GRAYSCALE IMAGE USING PARTICLE SWARM OPTIMIZATION M. S. CHELVA a1, S. V. HALSE b AND A. K. SAMAL c a SRTMU, Nanded, Maharasra, Inda b Karnaaka Sae Women's Unversy, Vjaypur, Karnaaka, Inda c Trden Academy of Technology, Bhubaneswar, Odsha, Inda ABSTRACT Evoluonary Approach found counless applcaons n varees felds due o her smplcy n undersandng, flexbly n mplemenaon and ease of adapng o he problem doman. In hs paper we propose a novel approach o enhance he qualy of grayscale mage usng parcle swarm opmzaon (PSO) algorhm. Here, we rea mage enhancemen as a non-lnear opmzaon problem and adop PSO o solve. The prme objecve of hs work s o devse an algorhm o mprove he vsual appearance qualy of an mage capured by he camera n a mcroconroller based embedded sysems, e.g., mage capured by drone, hand-held medcal magng devces, roboc mage capure applcaon, ec. Resuls of smulaon based expermenal sudy ndcae ha he proposed algorhm yeld accepable resul n erms of speed and mage qualy wh respec o s mplemenaon n a resrcve mcroconroller based envronmen. KEYWORDS: Image Enhancemen; Parcle Swarm Opmzaon (PSO) Algorhm; Evoluonary Algorhm Image Processng nvolves converng an mage no dgal form and performng some operaons on. Image Enhancemen s one of he mos mporan and dffcul echnques n mage research. The am of mage enhancemen s o mprove he vsual appearance of an mage, or o provde a beer ransform represenaon for fuure auomaed mage processng [Gora and Ghosh, 2009]. The ruhfulness of he resuls of mage processng depends upon how flawlessly he ncomng mage s presened o he sysem. Real-lfe mages are conamnaed wh varees of errors, dsoron and nose and also suffer from poor conras. Therefore, mage enhancemen plays a sgnfcan role n exracon of useful nformaon from [Kaur and Kaur, 2015], enhance he conras and remove he nose o ncrease mage qualy. The fundamenal reason of mage enhancemen s o brng ou deal ha s hdden n an mage ha s covered up n a pcure or o expand conras n a low conras pcure. Image enhancemen nvolves ransformaon echnques o devse new mage enhancemen mehod. One of he mos mporan sages n dgal mages processng applcaon s he mage enhancemen echnque whch mproves he qualy (clary) of mages for human vewng, removng blurrng and nose, ncreasng conras, and revealng deals are examples of enhancemen operaons. The enhancemen echnque dffers from one feld o anoher accordng o s objecve. The exsng echnques of mage enhancemen can be classfed no wo caegores: Spaal Doman and Frequency doman enhancemen [Umamageswar e. al., 2014]. Hsogram ransformaon s consdered as one of he fundamenal processes for conras enhancemen of gray level mages n spaal doman [Gora and Ghosh, 2009 & Behera e. al., 2015] whch faclaes subsequen hgher level operaons such as deecon and denfcaon. Lnear conras srechng employs a lnear ransformaon ha maps he gray-levels n a gven mage o fll he full range of values. Evoluonary algorhms have been prevously used o perform mage enhancemen [Kaur and Kaur, 2015] and n hs work, we have appled a global graylevels mage conras enhancemen echnque usng parcle swarm opmzaon (PSO) so as o bes f he demands of he human nerpreer [Sngh e. al., 2017]. The key movaon behnd selecng PSO as he ool o perform mage enhancemen s due o he fac ha PSO requres less number of operaon sapes as compared o GA (requre selecon, crossover and muaon operaons) and also PSO akes less me o converge o beer opma [Clerc and Kennedy, 2002]. The resuled gray-level enhanced mages by PSO are found o be beer compared wh oher auomac mage conras enhancemen echnques. Boh objecve and subjecve evaluaons are performed on he resuled mage whch says abou he goodness of PSO. The paper s organzed as follows. In secon II, we have presened a revew of leraures n he mage enhancemen problem doman. In Secon III, we dscuss necessary background conceps n deal and presen bascs of he PSO algorhm n Secon IV. In Secon V, we presen he proposed PSO based mehodology for smulaon sudy. Expermenal resuls are dscussed n Secon VI and Secon VII presens 1 Correspondng auhor

2 concludng remarks on he work presened hrough hs paper. SURVEY OF LITERATURE There are many approaches o manpulae he mages o enhance s qualy or serve as means of superor npu n erms of mage qualy for oher auomaed mage processng echnques. Man and Aggarwal [2010] n her paper presened a comprehensve revew of varous dgal mage enhancemen echnques adoped o provde a mulude of choces for mprovng he vsual qualy of mages. The paper provdes an overvew of facors suable o make approprae choce of mage enhancemen echnques nfluenced by he magng modaly, ask a hand and vewng condons. Paper by Gogna and Tayalm [2012] presens a comprehensve revew of he sudy of suably of varous evoluonary algorhms based meaheursc echnques such as Genec Algorhm (GA) and Dfferenal Evoluon Algorhm (DEA) for mage enhancemen applcaons. The resuls of mage enhancemen obaned usng dfferen evoluonary algorhms are compared amongs hemselves and also wh he oupu of hsogram equalzaon mehod. Kaur and Kaur [2015] n her paper have presened comprehensve revew of a couple of echnques such as Hsogram Equalzaon, Adapve Hsogram Equalzaon, ec. ha seek o mprove he vsual appearance of an mage or uplf he mage o a level ha s beer fed o furher analyss by an ndvdual or machne. Auhors Bed and Khandelwal [2013] have presened a crcal revew of varous mage enhancemen echnques n spaal doman and classfy hem wh an aemp o evaluae shorcomngs and fulfl he general needs n he feld of acve mage processng research. Umamageswar e. al., [2014] have presened applcaon of varous ransformaon echnques o devse new mage enhancemen mehod o enhance he mage as well as o preserve he nauralness of an mage from he sensaons of color perspecve. In he paper by Lee and Cho [2012] have proposes applcaon of DEA based auomac mage enhancemen echnque o acheve mprovemen n he mage capured by he camera sensor n moble devces. Subjecve es shows he usefulness of he DEA based approach proposed. Khunger e al. [2016] n her paper presened an embedded sysem applcaon where mage enhancemen echnques are adoped for brngng onlne correcons o he mages capured by auonomous underwaer vehcles ha ge blurred due o shadows generaed. Proposed approach adops conras equalzaon by conras lmed adapve hsogram equalzaon followed by Homomorphc flerng, wavele de-nosng, blaeral fler and hen applyng conras and nensy srechng for normalzng he color values. Performance of he proposed approach was assessed usng Peak Sgnal o Nose Rao (PSNR), Mean Square Error (MSE), and Sgnal o Nose Rao (SNR) of he mages and esablshed he valdy of he proposed approach. Auhor Nchal e. al., [2013] demonsraed a novel approach of enhancng conras qualy of gray scale mage n medcal magng, remoe sensng, and elecron mcroscopy and n many real lfe applcaons ha suffer from poor pcure conras. The purposed approach aemps dfferen mage enhancemen mehods o employs such as Hsogram Equalzaon (HE) and Dscree Wavele Transform (DWT) o ncrease mage vsbly and deals and her performance was comparavely suded consderng common facors such as enhancemen effcency, compuaonal requremens, nose amplfcaon, user nervenon and applcaon suably. Sahan e. al., [2015] n her paper presened a sudy referrng o mage processng applcaon n desgnng an embedded sysem wh smple nerface so ha he sysem s user frendly, where mage enhancemen feaure s of paramoun sgnfcance. Tradonal approaches such as Hsogram equalzaon (HE) and conras lmed adapve hsogram equalzaon (CLAHE) are found o exhb unnaural resuls. To mprove upon, auhors have suggesed a new mehod, known as conras lmed adapve hsogram equalzaon wh varable enhancemen degree (CLAHEwVED) ha gves beer resul as compared o HE and CLAHE. Chen e. al., [2004] presens sgnfcance of mage enhancemen n a fngerprn verfcaon sysem as a moble phone and embedded sysem applcaon and suggess a specal Gabor fler parameer selecon consran o reduce he compung complexy of he kernel generaon sep. Suggesed approach exhbs o acheve sgnfcan speed mprovemen and s almos as effecve as he radonal floang-pon based mplemenaon. Deborah e. al., [2010] n her paper presen approaches o enhance he performance of characer readably from old ypewren documens usng GA based opcal characer recognon by unng he fness funcon. The paper presens resuls of comparave sudy of several fness funcons and shows ha background varance (BV) s lkely o be he bes fness funcon as gves

3 he hghes correlaon. The PSO based mage enhancemen approach presened n hs paper s argeed mosly for embedded sysem applcaon. BACKGROUND CONCEPTS For mage enhancemen ask, a ransformaon funcon s requred whch wll ake he nensy value of each pxel from he npu mage and generae a new nensy value for he correspondng pxel o produce he enhanced mage. To evaluae he qualy of he enhanced mage auomacally, an evaluaon funcon s needed whch wll ell us abou he qualy of he enhanced mage. In hs secon we descrbe he funcon used for he proposed work. Transformaon Funcon Image enhancemen done on spaal doman uses a ransform funcon whch generaes a new nensy value for each pxel of he M N orgnal mage o generae he enhanced mage, where M denoes he number of columns and N denoes he number of rows. The enhancemen process can be denoed by: g(, = T[g(,] (1) where f(, s he gray value of he f(, h pxel of he npu mage and g(, s he gray value of he (, h pxel of he enhanced mage, T s he ransformaon funcon. Local enhancemen mehod apply ransformaon on a pxel consderng nensy dsrbuon among s neghborng pxels. Adapve hsogram equalzaon (AHE) s one such local enhancemen mehod whch gves good resul. However he expensve compuaonal overhead makes AHE a prohbve choce. The mehod used n hs paper s less me consumng and s smlar o sascal scalng [1]. The funcon used here s desgned n such a way ha akes boh global as well as local nformaon o produce he enhanced mage. Local nformaon s exraced from a user defned wndow of sze n n. The ransformaon T s defned as: g(, = K(,[f(, c m(,] + m(, a (2) In eq. (2), a and c are wo parameers, m(, s he local mean of he (, h pxel of he npu mage over a n n wndow and K(, s enhancemen funcon whch akes boh local and global nformaon no accoun, Expresson for local mean and enhancemen funcon are defned as: n 1 n 1 1 m (, = f ( x, y) (3) n n x= 0 y= 0 One form of he enhancemen funcon K(,, used n hs work s: k. D K (, = (4) σ (, + b where k, and b are wo parameers, D s he global mean and σ(, s he local sandard devaon of (, h pxel of he npu mage over a n n wndow, whch are defned as: M 1 N 1 1 D = f ( x, y) (5) M N = 0 j= 0 n n 1 2 σ (, = ( f ( x, y) m(, ) (6) n n x= 0 y= 0 The fnal form of he ransformaon funcon appears as gven below: k. D g (, [ f (, c m(, ] + m(, (, + b a = (7) σ Usng hs ransformaon eq. (7), conras of he mage s sreched consderng local mean as he cener of srech. Four parameers nroduced n he ransformaon funcon are: a, b, c and k rgger large varaons n he processed mage. Evaluaon Crera To evaluae he qualy of an enhanced mage whou human nervenon, we need an objecve funcon whch wll say all abou he mage qualy. Many objecve funcons have been suggesed n [Gora and Ghosh, 2009 & Behera e. al., 2015]. In hs paper, he objecve funcon s formed by combnng hree performance measures, namely enropy value, sum of edge nenses and number of edgels (edge pxels). I s observed ha compared o he orgnal mage good conras enhanced mage has more number of edgels [Gora and Ghosh, 2009 & Behera e. al., 2015] and enhanced verson should have a hgher nensy of he edges [Brak e. al., 2007]. Bu hese wo are no suffcen o es an enhanced mage; herefore one more measure has been aken no consderaon,.e. enropy value of he mage ha reveals he nformaon conen n he mage. If he dsrbuon of he nenses s unform, hen we can say ha hsogram s equalzed and he enropy of he mage wll be more. The objecve funcon consdered here s: n _ edgels( I s ) F ( Ie) = log(log( E( I s ))) H( Ie ) (8) M N In he above menoned eq. (8), I e s he enhanced mage of I o (he orgnal mage) produced by

4 he ransformaon funcon defned n eq. (7). In he above equaon eq. (8), he edges or edgels can be deeced by many effcen edge deecor algorhms such as Sobel, Laplacan, Canny, ec. In hs paper work, Sobel s used as an auomac hreshold deecor. Afer usng Sobel edge operaor we produce a Sobel edge mage I e. on he produced enhanced mage I e. E(I s ) s he sum of M N pxel nenses of Sobel edge mage I s. n_edgels(i s ) s he number of pxels n Sobel edge mage I s, whose nensy value s above a hreshold n he Sobel edge mage. Based on he hsogram, enropy value s calculaed on he enhanced mage I e as: 1 p f f ( x ) > f ( p ) p = (12) x, oherwse The value of g of he swarm n h eraon s obaned as: mn f ( p ) g = 1 g, NP = 1 1 f mn f ( p ) f ( g ) oherwse (13) 255 = = 0 H ( I e ) e (9) where e = h log 2 (h ) f h 0 oherwse e = 0, and h s he probably of occurrence of h nensy value of I e mage. PARTICLE SWARM OPTIMIZATION (PSO) PSO orgnally proposed by Kennedy and Eberhar [1995] s a novel evoluonary algorhm modelled afer he flockng behavour of brds. In PSO scheme, a swarm of brds (called parcles) represens he soluon space where each parcle represens a canddae soluon o he problem, characersed by s poson vecor x and velocy vecor v. The qualy or fness of he soluon ha each parcle mples s denoed by a funcon of s curren poson: f(x). Parcles n PSO follow a very smple mechansm o evolve her behavour: by ryng o mach he success of neghbourng parcles and her own success acheved. Consderng each parcle of dmenson n and he swarm of sze Np, he poson and velocy of he h parcle n he swarm, [1,Np], a he h generaon can be represened as: x = x, x,..., x ) and ( 1 2 v = v, v,..., v ) respecvely. Mahemacally, he ( 1 2 n new poson and velocy of each brd s represened as: n Fgure 1: Pseudo Code Algorhm for he PSO The pseudo code algorhm and he flowchar of he PSO as an ad o undersandng funconng of he algorhm s gven below n Fg. 1 and Fg. 2, respecvely: v = ω v + c r p x ) + c r ( g x ), (10) ( 2 2 x = x + v (11) where r 1,r 2 [0,1] are unform random numbers, ω s he nera wegh a he h eraon ha conrols he fludy of he medum n whch he brd moves, c 1 and c 2 are he posve acceleraon coeffcens ha conrols he convergence by drvng he parcle owards he local and global bes soluons, respecvely; v and x are he velocy and poson of he parcle a he h eraon; p and g are respecvely he parcle s personal and global bes poson aended so far. The rule for updang p of h parcle n h eraon s as follows: Fgure 2: Flowchar for he PSO

5 PSO BASED PROPOSED IE METHODOLOGY To produce an enhanced mage a ransformaon funcon defned n eq. (7) s used, whch ncorporaes boh global and local nformaon of he npu mage. The funcon also conans four parameers namely a, b, c, and k whch are used o produce dverse resul and help o fnd he opmal one accordng o he objecve funcon. These four parameers have her defned range whch s menoned n he parameer seng secon. Now our am s o fnd he bes se of values for hese four parameers whch can produce he opmal resul and o perform hs work PSO s used. P number of parcles are nalzed, each wh four parameers a, b, c, and k by he random values whn her range and correspondng random veloces. I means poson vecor of each parcle X has four componens a, b, c, and k. Now usng hese parameer values, each parcle generaes an enhanced mage. Qualy of he enhanced mage s calculaed by an objecve funcon defned Parameer Seng for PSO Algorhm The resul of PSO algorhm s very much parameer dependen. Fne unng of he parameers can provde beer resul han oher opmzaon algorhms. Parameer ω used n eq. (10) s called he nera wegh. Maxmum and mnmum value for ω s se o wo and zero respecvely, whch s same for all parcles. The process sars wh maxmum nera value and gradually reduces o mnmum. Therefore nally nera componen s bg and explore larger area n he soluon space, bu gradually nera componen becomes small and explo beer soluons n he soluon space. Inera value ω s calculaed as: ωmax ωmn ω = ωmax (14) max Parameers c 1, and c 2 are posve acceleraon consans, gven a random number n he range [0,2]. Throughou he lfeme of a parcle, hese parameer values are fxed for each parcle. r 1 and r 2 are random numbers n he range [0,1] and vares for each componen of he parcles n every generaon (.e., durng each eraon). In hs work here are four problem specfc parameers, a, b, c and k. The range for hese parameers are he same for all,.e., n he range [1,5]. a [0.5,1.5], b [0,0.5], c [0,1] and k [0.5,1.5]. We have changed he range for parameer as ha range does no produce good resul. I has been observed ha small value for srech he nensy n a large amoun. So, afer normalzng hose nensy values beween [0,255] o dsplay he mage, becomes very dscrzed and he orgnaly of he mage s los. So, we ncrease he range o overcome hs specfc problem, and he range s fxed o [1,(D/2)], where D s he global mean of he orgnal mage. RESULTS ANALYSIS In hs secon, we presen he expermenal resuls for evaluang he effcency of he proposed auomac mage enhancemen mehod. The mplemenaon of he proposed algorhm s done n MATLAB 7.8 usng a compuer wh Inel 3 Processor (2.20GHz) and 4 GB RAM. For geng he expermenal resul from he smulaon sudy of he proposed mehod, we have consdered hree sample es mages whch nclude mage aken n ndoor as well as oudoor. Fgures below show varey of source (fg. 4, 6 and 8) and enhanced mages (fg. 5, 7, and 9) chosen for conducng he smulaon sudy. Plos presened n fgures 10, 11 and 12 represens her correspondng mprovemen n fness value. Fgure 3: PSO based Image Enhancemen Algorhm Fgure 4: Source Image-1 Fgure 5: Oupu Image-1

6 Fgure 6: Source Image-2 Fgure 7: Oupu Image-2 Fgure 10: Image Enhancemen Performance Plo-1 Fgure 8: Source Image-3 Fgure 9: Oupu Image-3 Performance of he smulaon was suded from he mprovemen n he objecve funcon as well from he vsual enhancemen. For he paramerc sudy of performance, varance n he nensy of neghbourng value pxel over an mage block of n n wndow wh n = 3 was aken n boh npu mage and enhanced mage. Fas rse and sablzaon n he fness value as shown n he fness versus eraon plos (fg. 10, 11 and 12) ndcaes effecveness of he proposed approach. From he vsual analyss of he enhanced mage agans her correspondng npu mages appropraely maches wh he performance plos. Fgure 11: Image Enhancemen Performance Plo-2 In he execuon of program we bascally have aken n average 50 populaons and 50 eraons o execue he operaon. Resuls of smulaon has esablshed ha a fas rse n fness value (as low as 4 eraons) can make suable for mplemenaon n low speed and low memory foo-prn embedded processor envronmens. The resul of smulaon shows he npu mage along wh he enhanced mage as well as he performance plos. The performance plo represens he number of eraon n x axs and he fness values n y axs. Fgure 12: Image Enhancemen Performance Plo-3 CONCLUSION In hs paper, a have nroduced a new approach o acheve auomac mage enhancemen

7 usng real-coded wegh adjused PSO, mplemened by specfyng a suable fness funcon proporonal o he number and nensy values of he edgel pxels and o he enropc measure of he mage. The objecve of he algorhm was o maxmze he oal number of pxels n he edges hus beng able o vsualze more deals n he mages. The algorhm s esed on hree seleced mages. The resuls obaned are abulaed and compared wh he resuls obaned usng oher radonal and evoluonary approaches. I s clear from he obaned resuls ha he proposed PSO based mage enhancemen s beer han oher mage enhancemen echnques mplemened usng oher approaches n erms of boh qualy of he soluon and compuaonal effcency. The proposed PSO based mage enhancemen scheme may be exended n several ways, such as: fne unng of he PSO parameers n order o reduce he number of parcles and reducng he maxmum number of eraons. Anoher exenson s o code local parameers of he mehod ha apples o each neghbourhood. REFERENCES Gora A. and Ghosh A., Gray-level Image Enhancemen by Parcle Swarm Opmzaon, Proceedngs of he Conference of IEEE World Congress on Naure & Bologcally Inspred Compung, pp Kaur A. and Kaur M., Revew of Image Processng- Inroducon o Image Enhancemen Technques usng Parcle Swarm Opmzaon, Inernaonal Journal of Arfcal Inellgence and Applcaons for Smar Devces, 3(1): Sngh N., Kaur M. and Sngh K.V.P., Parameer Opmzaon In Image Enhancemen Usng PSO, Amercan Journal of Engneerng Research, 2(5): Brak M., Shea A. and Ayesh A., Image Enhancemen Usng Parcle Swarm Opmzaon, Proceedngs of he World Congress on Engneerng 2007 Vol I, WCE 2007, July 2-4, 2007, London, U.K., Behera S.K., Mshra S. and Rana D., Image Enhancemen usng Acceleraed Parcle Swarm Opmzaon, Inernaonal Journal of Engneerng Research & Technology, 4(3): Man R. and Aggarwal H., A Comprehensve Revew of Image Enhancemen Technques, Journal of Compung, 2(3):8-13. Gogna A. and Tayalm A., Comparave Analyss of Evoluonary Algorhms for Image Enhancemen, Inernaonal Journal of Meaheurscs, 2(1): Kaur K. and Kaur K., Recen Trends Towards Improved Image Enhancemen Technques, Inernaonal Journal of Advanced Research n Compuer Scence and Sofware Engneerng, 5(5): Bed S.S. and Khandelwal R., Varous Image Enhancemen Technques- A Crcal Revew, Inernaonal Journal of Advanced Research n Compuer and Communcaon Engneerng, 2(3): Umamageswar D., Svasakh L. and Van R., Qualy Analyss and Image Enhancemen Usng Transformaon Technques, Inernaonal Journal of Advanced Research n Elecrcal, Elecroncs and Insrumenaon Engneerng, 3(3): Lee M.-C. and Cho S.-B., Ineracve Dfferenal Evoluon for Image Enhancemen Applcaon n Smar Phone, Proceedngs of he IEEE World Congress Conference on Compuaonal Inellgence, WCCI 2012, June, 10-15, Brsbane, Ausrala, pp Khunger S., Sharma K. and Verma A., A Novel Approach for Underwaer Image Enhancemen, Inernaonal Journal for Scenfc Research & Developmen, 4(2): Nchal A., Kalamkar P., Lokhande A., Pal V. and Salunkhe B., A Novel Approach o Medcal & Gray Scale Image Enhancemen, Inernaonal Journal of Engneerng Research and Applcaons, 3(3): Sahan M., Rou S.K., Sapahy L.M. and Para A., Desgn of an Embedded Sysem wh Modfed Conras Lmed Adapve Hsogram Equalzaon Technque for Real- Tme Image Enhancemen, Proceedngs of he Inernaonal Conference on Communcaons and Sgnal Processng (ICCSP)-2015, 2-4 Aprl, 2015, pp Chen J. S., Moon Y. S. and Fong K. F., Effcen Fngerprn Image Enhancemen for Moble Embedded Sysems, Lecure Noes n Compuer Scence, LNCS 3087, 3087:

8 Deborah H., Manurung R. and Arymurhy A.M., Objecve Crera for Typewren Old Documen Image Enhancemen & Resoraon, "Proceedngs of he Inernaonal Conference on Advanced Compuer Scence and Informaon Sysem (ICACSIS 2010), A Bal, Indonesa Kennedy J. and Eberhar R. C., Parcle Swarm Opmzaon, n: Proc. of IEEE Inernaonal Conference on Neural Neworks, Pscaaway, NJ. pp Clerc M. and Kennedy J., The parcle swarmexploson, sably, and convergence n a muldmensonal complex space, IEEE Trans. Evol. Compu., 6(1):58-73.