Color Consistency Correction Based on Remapping Optimization for Image Stitching

Transcription

1 Color Consstency Correcton Based on Remappng Optmzaton for Image Sttchng Menghan Xa Jan Yao* Renpng Xe M Zhang School of Remote Sensng & Informaton Engneerng, Wuhan Unversty, Wuhan, Hube, P.R. Chna *Emal: jan.yao@whu.edu.cn Web: Jnsheng Xao Electronc Informaton School, Wuhan Unversty, Wuhan, Hube, P.R. Chna Abstract Color consstency correcton s a challengng problem n mage sttchng, because t matters several factors, ncludng tone, contrast and fdelty, to present a natural appearance. In ths paper, we propose an effectve color correcton method whch s feasble to optmze the color consstency across mages and guarantee the magng qualty of ndvdual mage meanwhle. Our method frst apply well-drected alteraton detecton algorthms to fnd coherent-content regons n nter-mage overlaps where relable color correspondences are extracted. Then, we parameterze the color remappng curve as transform model, and express the constrants of color consstency, contrast and gradent n an unform energy functon. It can be formulated as a convex quadratc programmng problem whch provdes the global optmal soluton effcently. Our method has a good performance n color consstency and suffers no pxel saturaton or tonal dmmng. Expermental results of representatve datasets demonstrate the superorty of our method over state-of-the-art algorthms. 1. Introducton As the growng popularty of mage capture equpments and photo sharng, we now are n an mage world where the mage data can be obtaned easly by usng SLR cameras, smart phones, or downloadng from the socal networks and publc data platforms 1. Ths exctng data avalablty enables many vsual applcatons, such as mage recognton [7], panoramc magng [1, 11, 27], mage renderng [31, 32] and vrtual navgaton [19, 9]. In many cases, mages of the same scene mght show notceable tonal nconsstence because of dfferent atmosphere llumnaton, exposure tme and camera response functon. Such photo- 1 USGS (satellte mage): Fgure 1: Panorama composted wth source mages taken by cameras wth dfferent magng settngs. Color nconsstence s stll notceable n the scene even processed by seamlne selecton and mult-band blendng n Enblend 2. metrc dfference could partcularly nfluence the vsual effects of multple mages based renderng msson [29, 18]. Ths paper focus on studyng the color correcton problem for multvew mage sttchng, as exemplfed n Fgure 1. Wthn the technque ppelne of mage sttchng, color correcton s a crtcal step n presentng the composted mage wth an natural and consstent tone. Apart from removng the overall tonal dsparty, t also facltates the followng seamlne selecton [10] and blendng [22]. However, obtanng a satsfactory color correcton result s a non-trval task: For one thng, a rgorous correcton model that can genunely express the nter-mage color transformng relatons remans an open ssue; For another, the orgnal dstrbuton of pxel value ndcates semantc nformaton (object structure, contrast, saturaton, etc), whch mght mght be degraded or destroyed when adjustng pxels for the least color dsparty. Ths s a solved problem for two mage nvolved color transfer technques that allows complex operatons to enhance process qualty, however t s hard to be extended to multple mages scheme unformly. Cascadng manpulaton strategy [14, 26] subjects to lnearly amplfed accumulaton error, makng the global consstency nacces- 2 Avalable at:

2 sble. As for multvew color correcton, exstng methods manly employed smple correcton models or neglected detal preservaton and contrast consderaton n the global optmzaton for consstency [29, 18]. Thus, a robust and effectve multvew color correcton algorthm remans to be nvestgated further. As to the problems stated above, ths paper presents an effectve method ntegratng detal preservaton, global contrast and color consstency nto an unform optmzaton framework. It can guarantee the optmalty of the processng result. Lke regular approaches, we leverage the color statstcs of overlap regons to obtan the color correspondences. Specally, to mprove the accuracy and robustness of ths, the classc Iteratvely Reweghted Multvarate Alteraton Detecton (IR-MAD) [13] s used to detect and remove the dsturbance of altered content n overlaps. Inspred by the flexble model used n [5], we parameterze the quadratc splne curve as transform functon, whch can express dfferent constrants n the optmzaton functon effectvely. Besdes, the optmzaton problem can be solved effcently by vrtue of convex quadratc programmng. Through comparng to the latest methods, our approach llustrated better performance n color consstency and dynamc range optmzaton. The rest of the paper s structured as follows. In Secton 2 an overvew of related work s gven. In Secton 3 the scope of the problem s formally defned and n Secton 4 the proposed color correcton approach s detaled. In Secton 5 expermental results are evaluated. Fnally, conclusons and future work are presented n Secton Related Works Regardng color processng, there are two relevant technques: two mage based color transfer [4, 8], and multvew color correcton [1, 6, 15]. Exstng methods are revewed below respectvely Color Transfer The concept of color transfer was frst proposed by Renhard et al. [4], whch ams at propagatng one mage s color characterstc to another. From ths baselne approach, many other followng works were proposed. Before 2010, they manly focus on solvng the decorrelaton between color channels [16] and content-based elaboratng color transferrng [23], whch were well summarzed n [30]. Snce then, more emphass was lad on gran-free, detal preservaton and artfacts suppresson. To heghten the detal performance, Xao et al. [28] proposed a gradentpreservng model to convert the transfer processng to an optmzaton balancng the color dstrbuton and the detal performance. Dfferent from a stepwse strategy, Su et al. [20] proposed to perform color mappng and detal boostng separately through gradent-aware decomposton, to obtan a gran-free and detal preserved result. Based on ths dea, they developed more complete framework to suppress corruptve artfacts [21]. To avod color dstorton, Nguyen et al. [12] appled Xao et al. s algorthm [28] n lumnance channel, followed by the color gamut algnment va rotatng around lumnance axs. It was effectve n preventng color dstorton but lackng n color fdelty because of ts smple color transform model. To make a more accurate color mappng, Hwang et al. [8] proposed to correct each pxel s color wth an ndependent affne model, whch s the soluton of probablstc movng least square based on feature color correspondences. Besdes, some works presented hgh-level color transfer applcaton based on semantc segmentaton or content recognton [25, 3]. Dfferent wth these tradtonal methods, learnng-based color transfer methods were attempted to tran out the proper color mappng relatonshp [24, 2] Multvew Color Correcton Here, multvew color correcton refers to correct the color of three or more mages for consstency n a global way, whch excludes applyng color transfer across mage sequence or network to realze color consstency. Brown et al. [1] frst proposed to performed the gan compensaton for multple mages n the way of global optmzaton, whch has been appled n panorama software Autosttch. Under the lnear optmzaton framework, Xong et al. [29] extended ths method by employng gamma model n lumnance channel and lnear model n chromatc channels. Qan et al. [17] presented a manfold method to remove color nconsstence, whch made full use of both the correspondences n overlap regons and the ntrnsc color structures. However, ts requrement on accurate geometrc algnment lmted ts applcaton range. In 3D modelng, Shen et al. [18] exploted lnear functon as color correcton model over color hstogram to generate consstent textures. It s effcent but unable to process great color dscrepancy. In photo edtng applcatons, Hacohen et al. [6] proposed to model the remappng curve drectly, whch were optmzed for consstent appearance of photos usng color correspondences obtaned from nonrgd dense matchng [5]. Ths color model s flexble enough to correct even large tonal dsparty but ts dependence on dense matchng makes t computatonally expensve. To address t, Park et al. [15] presented a robust lowrank matrx factorzaton method to estmate ts correcton parameters, whch just need sparse feature matchng. But, ts color correcton ablty s not as hgh. 3. Problem Formulaton Frst of all, our color correcton method ams to be appled on the sequental mages whose algnng models have 22978

3 !).%//)0'"$()$*"(+',%&-) # #"$%&'"$()$*"(+',%&-)!$% Fgure 2: Pecewse quadratc splne: the color mappng functon used n our method. Leveragng the fxed horzontal dstrbuton of red anchor knots, the curve just covers the doman of the orgnal ntensty values[v mn,v max ]. been estmated n advance. Thus, the adjacent relatonshps and overlap regons are used as gven nformaton n our approach. Second, our color correcton algorthm runs on the assumpton that all the color nconsstence among mages s caused by dfferent magng condtons whch affect each mage as a whole. Seekng for the optmal consstency, our approach expresses all the qualty requrements n the form of constrant on model parameters, whch are then solved n a global optmzaton. To realze ths, we defne the transformaton model as three monotoncally ncreasng mappng curves (one per channel), each of whch s formulated as a pecewse quadratc splne wth m control knots (m = 6 as default). As llustrated n Fgure 2, these red knots {(ν k, ν k )} m k=1 are half free on the coordnate plane, where {ν k } m k=1 are fxed evenly on the horzontal axs to control the mappng curve effectvely, whle{ ν k } m k=1 are free to determne the shape of the mappng curve as the actual model parameters. Thus, the color mappng functon for mage I can be parameterzed as: F = arg{( ν 1, ν 2,..., ν m) c } 3 c=1, (1) where c s the ndex number of each channel. Partcularty, havng the curve cover the ntensty doman of each orgnal mage can save the troublesome extrapolaton for non-overlap regons. Besdes, the detaled defnton of our optmzaton functon are descrbed n Secton Color Correcton Our color correcton method conssts of two steps: color correspondence extracton and model parameters optmzaton. Partcularly, the color correcton s performed n Y CbCr space, because ts lumnance channel and chromatc channels are separated and each channel has specfc upper and lower bounds, whch facltate the qualty-aware color #$%&'(! 34++!-#!5$67#8$94$/'"2 Fgure 3: Vsual comparson between the corrected result wth alteraton flterng mask appled and that wth none mask appled when alteraton exsts. consstency optmzaton. )!*!+!,-!$%&'(! 4.1. Relable Color Correspondence Based on the adjacent relatons, we extract color correspondences n the overlaps of each mage par. For effcency problem, we adopt the statstcal measures of color hstogram, nstead of matchng mage color by pxels. That s to say, we take the same quantles (color value pars of the same frequency) n the cumulatve color hstograms of the shared contents as correspondences. In general case, the overlap regons can be regarded as the shared contents, even f a bt of msalgnment exsts. However, f obvously altered objects exst there, ther pxels as outlers should be excluded from the overlaps n advance of hstogram countng. To do so, we utlze a famous alteraton detecton algorthm IR-MAD [13] n overlap regons to generate flterng masks. However, t s computatonally expensve to run IR- MAD on the mages of ther orgnal sze, especally for hgh-resoluton remote sensng mages. Thus, we downsample each overlap regon nto the sze of 250K pxels at frst and then apply the IR-MAD on them to get a coarse mask. Here s an example that applyng alteraton flterng mask mproves the accuracy of color correcton result when alteraton exsts n Fgure Remappng Model Optmzaton Wth color correspondences of mage pars, energy functon can be desgned by usng the parameters that reman to be optmzed. Gven a group of mages{i } n =1, we attempt to seek a group of color transformatons {F } n =1 whch mantans a good balance between three qualty goals: color: pxels depctng the same content have the same color across mages; gradent: orgnal structural detals reman on the transformed mages; contrast: all the transformed mages have a reasonable wde dynamc range; %)./01$/'"2 34++!-#!5$67#8$/'"

4 As stated n Secton 3, all these qualty requrements should be expressed as the constrants on parametrc model. In our method, the global optmzaton s conducted ndependently n each channel. Wthout losng generalty, the transformaton functon F of I n certan channel s denoted as f = arg( ν 1, ν 2,..., ν m) for smplcty. So, n any channel, the energy functon can be formulated as: E = I I j w j E data (f,f j )+λ n E regulaton (f ) =1 subject to: C rgd (f ), [1,n], (2) wherew j s the weght proportonal to the area of the overlap regon between I and I j ( w j = 1), and λ serves to balance the data term and the regulaton term. In our experments, λ = ξ M m s used, where M denotes the amount of color correspondences extracted n each overlap (M = 16 as default) and ξ [0.5,5] s recommended. Actually, our algorthm s nsenstve to λ snce ths two terms are noncontrary. The color consstency across mages s expressed by data term that penalzes the devaton between remapped correspondng color values: E data (f,f j ) = M k=1 f (vk) f j (v j k ) 2, (3) where {v k,vj k }M k=1 are color correspondences between I and I j. As an unary term, E regulaton (f ) enforces certan constrants on the color transformatons softly, ncludng regularzng the parameters and stretchng dynamc range. E regulaton (f ) = m 1 f ( v k) v k) 2 k=1 η f (v 0.05) f (v 0.95) 2, (4) where { v k }m 1 k=1 denote the horzontal coordnates of jont ponts jonng dfferent local curve segments (marked n dfferent colors n Fgure 2). Exactly, we have v k = ν k +ν k+1 2,k = {1,2,...,m 1}. The mportance of ths term les n two aspects: () keepng transformed mages close to ther orgnal appearance slghtly as default soluton; () all the model parameters are optmzed as a whole even f no color correspondences falls nto the scope of some anchors (parameters). In addton, the latter negatve term prevents the dynamc range to narrow down where vα depcts the α- percentle of the CDF of I. Ths term s very necessary to avod an easy-appearng optmzng result that corrected mages present a consstent but dmmng tone, because remappng ntensty nto a lower range conforms to the mnmal energy prncple ofe color. Partcularly,η = 5 andη = 0 are used n lumnance channel and chromatc channels respectvely. Namely, we only mpose dynamc range constrant n lumnance.!#$%&'() *'++)",- *'++)", #$ *'++)",. Fgure 4: Channel nformaton dsplay of a color mage n YCbCr space. The major gradent nformaton s contaned n lumnance channel Y. As a qualfed color mappng curve, the quadratc splne should meet two basc requrements: ncreasng monotoncty and mappng doman lyng wthn gamut. They are guaranteed through the followng constrant term: C rgd (f ) : { τb f (v k) τ u, v k [v mn,v max ], v start f (v 0.01), f (v 0.99) v end, where τ b and τ u defnes the bottom and upper boundary of the mappng curve s slope doman, and [v start,v end ] s the gamut range of related channel. Actually, the defnton of slop doman, especally ts bottom boundary, s a trade-off problem between detal preservaton and color consstency, snce a hgher value of bottom boundary (such as τ b 1.0) can prevent gradent detal loss from ntensty levels mergng, whle t also nevtably restrcts the freedom of parameters to acheve a better color consstency. In our method, we set the slop doman [0.3,5] for chromatc channels Cb and Cr, and[0.5,5] for lumnance channely where most of the gradent nformaton s contaned, as the example shown n Fgure Implementaton Detals Our correcton functon, as a pecewse nterpolaton model, can not be expressed explctly. We now descrbe how to express the functon mappng f and partal dervatvef wth the actual model parameters { ν 1, ν 2,..., ν m}. Gven a color value vk, supposng t falls n the control scope of knots {(νp, ν p)} p+2 p, then ts remapped value ṽk can be obtaned va the followng quadratc splne nterpolaton equaton: vk = 1 2 [(1 2t+t2 )νp +(1+2t 2t 2 )νp+1 +t 2 νp+2], ṽk = 1 2 [(1 2t+t2 ) ν p +(1+2t 2t 2 ) ν p+1 +t 2 ν (6) p+2], where the only unknowns are nterpolaton coeffcent t [0,1] and ṽk, so ṽ k can be expressed by model parameters { ν p} p+2 p, whch s the essence off. In fact, we tend to solve the nterpolaton coeffcents of all color correspondences before the global optmzaton, then the functon mappng equals to a lnear nterpolaton. Accordng to Eq. (6), we can express the slope value of (5) 42980

5 remappng curve atv k as: f (v k) = ṽ / t v / t = ( ν p 2 ν p+1 + ν p+2)t+ ν p+1 ν p (νp 2νp+1, +ν p+2 )t+ν p+1 ν p (7) where νp 2νp+1 + νp+2 = 0 snce {νp} m =1 are constants dstrbutng on the horzontal axs evenly (as shown n Fgure 2). So, f (v k ) equals to a lnear functon wth t [0,1] as the free varable. Then, the doman constrant f [τ b,τ u ] can be expressed as lnear nequaltes: τ b ν p+1 ν p ν τ u, t = 0, p+1 ν p (8) τ b ν p+2 ν p+1 νp+1 τ u, t = 1, ν p So far, we can substtute Eq. (6) and Eq. (8) nto Eq. (3), Eq. (4) and Eq. (5). Then, the energy functon Eq. (2) turns a quadratc polynomal, whch can be transformed to the standard form of constraned quadratc programmng, then s mnmzed by convex quadratc programmng 3 effcently. 5. Expermental Results We compare the proposed approach aganst two latest methods n correctng color consstency of multple mages. The one s lnear model based Shen et al. s method [18], the other s albedo reflect model based Park et al. s method [15]. As two typcal applcatons of mage sttchng, ground panorama and remote sensng mage mosackng are used to evaluate the performance of color correcton approaches. We use the default values of parameters descrbed n Secton 4.2 for all the experments. Generally, makng a vsually pleasng consstent result s the prmary goal of color correcton for mage sttchng. Thus, vsual effects serve as the major evaluaton crteron of algorthms. As supplement, quanttatve evaluatons on color Dscrepancy (CD) and gradent loss (GL) are also conducted, as the metrc equatons show: CD = H(Îj,Îj) w j, N bn I I j (9) GL = 1 n Go(I,Î), n N px =1 where Î depcts the corrected mage of source mage I, and Îj denotes the overlap regon of Î shared wth Îj. w j s a normalzed weght proportonal to the area of the overlap between I and I j ( w j = 1). H( ) calculates the dfference between hstograms of mages by bns, whle Go( ) computes the dfference between gradent orentaton maps of mages by pxels. N bn s the number of the bns of a hstogram, and N px s the amount of pxels of I. 3 QuadProg++: Fgure 5: Color correcton results of LUNCHROOM: edted mages (a), corrected results of [18] (b), [15] (c) and ours (d). For comparson s sake, regons wth tonal nconsstency and color cast are marked wth red boxes and green boxes respectvely Strp Panorama of Ground Scenes We frst demonstrate the performance of our color correcton method on a publc panorama dataset LUNCH- ROOM (selected 15 mages). Snce the orgnal mages have lttle tonal dfference, we delberately modfy ther color CDF ndependently n Photoshop, n order to test the algorthms robustness under drastc color and llumnaton varatons. The edted mages and corrected results are shown n Fgure 5 n the form of algned panorama. Here, to compare color dsparty between adjacent mages, we sttch mages nto a panorama wth no boundary fuson appled. Inspectng the results qualtatvely, we see that despte the huge nput varablty, the color and lumnance consstency mproved greatly on the corrected mages. In Fgure 5, we can fnd (d) has the best color consstency, whch s also attested n Table 1. Also, on overall vsual effect, (d) s slghtly superor to (c) and obvously better than (b) that shows knd of dark tone and resdual color cast. Our flexble correcton model and energy term consderng dynamc range make effects here. As for gradent preservaton, Table 1 shows [15] has the best performance. Ths mght be explaned by that ts albedo reflect based correcton model s more close to the real magng prncple. Due to the curve slop constrant #" $" %"

6 Table 1: Quanttatve comparson on color corrected results obtaned through dfferent methods. Color dscrepancy (CD), gradent loss (GL) n Eq. (9) and runnng tme (unt: second) are evaluated comprehensvely. Methods Input Shen et al. s [18] Park et al. s [15] Our approach LUNCHROOM CAMPUS ZY-3 UAV #CD #GL #Tme #CD #GL #Tme #CD #GL #Tme #CD #GL #Tme #" $" %" Fgure 6: Color correcton results of CAMPUS: source mages (a), corrected results of [18] (b), [15] (c) and ours (d). For comparson s sake, regons wth notceably resdual tonal dfference are marked wth red boxes Block Mosac of Remote Sensng Images we need to correct the color dsparty of mages acqured at dfferent tmes (even dfferent seasons) to show consstent tone. Here, we experment on 16 mult-temporary mages acqured by Chnese ZY-3 satellte, whch present obvous color dsparty as shown n Fgure 7 (a). The correspondng corrected results are depcted as (b), (c) and (d) n Fgure 7, from whch we can fnd that (d) has an obvous superorty over (b) and (c) n tonal consstency. In contrast, (b) has some overly dark regons and (c) has resdual yellow tone uncorrected (marked n green boxes) and notceable tonal dfference between mage strps. In [18], the ntensty of all the corrected mages wll be stretched lnearly to the gamut by fxed coeffcents after global optmzaton, whch turns (b) nto the hghest contrast but meanwhle darkens some regons overly. Ths mght also be the reason that [18] has less gradent loss than our method n dataset ZY-3. Sttchng block-layout mages usually happens n remote sensng mage mosackng, where each source mage has more neghbors than n strp panorama. In some occason, Further on, we analyze the performance of our method on a larger dataset UAV (130 mages) captured by unmanned aeral vehcle. Smlar to LUNCHROOM, the color dffer- preventng ntensty mergng, our method outperforms lnear model based [18] n gradent preservaton slghtly. We further test our method on another publc dataset CAMPUS (18 mages) wth contnuous lumnance varaton. The source mages and ther corrected results are dsplayed n Fgure 6. It s easy to observe that (d) shows the best tonal consstency, llustratng the outstandng ablty of our method n mnmzng color dsparty. Snce lttle color cast exsts n the orgnal mages, the corrected result (b) of [18] shows better consstency than (c) of [15], such as the regons marked wth red boxes n Fgure 6. Note that although no approach removes the tonal dfference from mages completely, the resdual nconsstence after correcton wll be low enough to conceal through fuson algorthms

7 #" %" $" Fgure 7: Color correcton results of ZY-3: source mages (a), corrected results of [18] (b), [15] (c) and ours (d). For comparson s sake, regons wth tonal nconsstency and color cast are marked wth red boxes and green boxes respectvely. #" $" %" Fgure 8: Color correcton results of UAV: edted mages (a), corrected results of [18] (b), [15] (c) and ours (d). For comparson s sake, regons wth color dfference and abnormal tone are marked wth red boxes and green boxes respectvely. ences among mages n UAV are amplfed va our random tone procedure. The correcton results are compared n Fgure 8. Because of the global optmzaton scheme, all the methods exhbt lttle performance declnng on the large dataset, except for (b) n Fgure 8 showng dramatc lumnance polarzaton. Ths s because [18] only consders the nter-mage color dsparty but neglect constrant on ndvdual mage s dynamc range. Dfferently, our optmzaton functon has a specal energy term that penalzes each mage s dynamc range to narrow down, whch makes good effect n addressng ths problems. As (c) shows, [15] has a good effect n color saturaton, despte of ts nferor tonal consstency. Besdes, some source mages of low-qualty gradent structures n UAV make the numercal result n gradent loss (GL) knda abnormal and meanngless. Conclusvely, [18] shows a better ablty n mprove the color consstency than [15], whle [15] s superor to [18] n guaranteeng good qualty of corrected mages, such as gradent preservaton, natural contrast and color fdelty. Due to the qualty-aware energy functon, our method makes the best effects n generatng a vsually pleasng correcton result. Runnng Tmes. Our method and Shen et al. s method [18] are mplemented n C++, whle the code of Park et al. s method [15] s provded n Matlab mplementaton. All the procedures are tested on a desktop PC wth Intel GHz and 8 GB RAM, whose runnng tme on the four datasets s depcted n Table 1. In our method, color correspondences extracton takes the major computaton, and closed-form soluton makes our method surpass [18] whose parameters of lnear model are optmzed by non-lnear functon. As [15] was mplemented n Matlab, we turn to analyze ts algorthm complexty whch reflects the runnng effcency n sgnfcant degree. Structure from moton (SFM) based color correspondence and ts nner teratve scheme of matrx decomposton defntely make the computaton cost of [15] much hgher than other methods

8 6. Conclusons We have presented an effectve method to optmze the color consstency across multple mages. Our key contrbuton les n the novel energy functon desgn based on the parameterzed quadratc splne model, whch turns major semantcally vsual requrements nto effectve parametrc expresson. Convex quadratc programmng gves the global optmal soluton quckly. Several typcal expermental results verfed the valdty and generalty of the proposed method. Comparng to exstng methods, our method leads n the performance of both effectveness and effcency. However, sngle-channel optmzaton strategy can not solve the color cast (whte balance) problem essentally. Mult-channel jont adjustment or response functon calbraton mght be helpful solutons, whch wll be nvestgated n the future works. Acknowledgments Ths work was partally supported by the Natonal Natural Scence Foundaton of Chna (Project No ), the Hube Provnce Scence and Technology Support Program, Chna (Project No. 2015BAA027), the Natonal Natural Scence Foundaton of Chna under Grant , LIESMARS Specal Research Fundng, and the South Wsdom Valley Innovatve Research Team Program. References [1] M. Brown and D. G. Lowe. Automatc panoramc mage sttchng usng nvarant features. Internatonal Journal on Computer Vson (IJCV), 74(1):59 73, [2] V. Bychkovshy, S. Pars, E. Chan, and F. Durand. Learnng photographc global tonal adjustment wth a database of nput/output mage pars. In CVPR, [3] O. Frgo, N. Sabater, V. Demouln, and P. Heller. Optmal transportaton for example-guded color transfer. In Asan Conference on Computer Vson (ACCV), [4] B. Gooch, M. Ashkhmn, E. Renhard, and P. Shrley. Color transfer between mages. IEEE Computer Graphcs and Applcatons (CGA), 21(5):34 41, [5] Y. HaCohen, E. Shechtman, D. B. Goldman, and D. 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