Object Removal by ExemplarBased Inpainting


 Kathryn Lucas
 2 years ago
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1 Oject Removl y ExemplrBsed Inpinting A. Criminisi, P. Pérez K. Toym Microsoft Reserch Ltd., Cmridge, UK Microsoft Corportion, Redmond, WA, USA Astrct A new lgorithm is proposed for removing lrge ojects from digitl imges. The chllenge is to fill in the hole tht is left ehind in visully plusile wy. In the pst, this prolem hs een ddressed y two clsses of lgorithms: (i) texture synthesis lgorithms for generting lrge imge regions from smple textures, nd (ii) inpinting techniques for filling in smll imge gps. The former work well for textures repeting twodimensionl ptterns with some stochsticity; the ltter focus on liner structures which cn e thought of s onedimensionl ptterns, such s lines nd oject contours. This pper presents novel nd efficient lgorithm tht comines the dvntges of these two pproches. We first note tht exemplrsed texture synthesis contins the essentil process required to replicte oth texture nd structure; the success of structure propgtion, however, is highly dependent on the order in which the filling proceeds. We propose estfirst lgorithm in which the confidence in the synthesized pixel vlues is propgted in mnner similr to the propgtion of informtion in inpinting. The ctul colour vlues re computed using exemplrsed synthesis. Computtionl efficiency is chieved y locksed smpling process. A numer of exmples on rel nd synthetic imges demonstrte the effectiveness of our lgorithm in removing lrge occluding ojects s well s thin scrtches. Roustness with respect to the shpe of the mnully selected trget region is lso demonstrted. Our results compre fvorly to those otined y existing techniques. 1. Introduction This pper presents novel lgorithm for removing ojects from digitl photogrphs nd replcing them with visully plusile ckgrounds. Figure 1 shows n exmple of this tsk, where the foreground person (mnully selected s the trget region) is replced y textures smpled from the reminder of the imge. The lgorithm effectively hllucintes new colour vlues for the trget region in wy tht looks resonle to the humn eye. In previous work, severl reserchers hve considered texture synthesis s wy to fill lrge imge regions with Figure 1: Removing lrge ojects from imges. () Originl imge. () The region corresponding to the foreground person (covering out 19% of the imge) hs een mnully selected nd then utomticlly removed. Notice tht the horizontl structures of the fountin hve een synthesized in the occluded re together with the wter, grss nd rock textures. pure textures repetitive twodimensionl texturl ptterns with moderte stochsticity. This is sed on lrge ody of texturesynthesis reserch, which seeks to replicte texture d infinitum, given smll source smple of pure texture [1, 8, 9, 10, 11, 12, 14, 15, 16, 19, 22]. Of prticulr interest re exemplrsed techniques which cheply nd effectively generte new texture y smpling nd copying colour vlues from the source [1, 9, 10, 11, 15]. As effective s these techniques re in replicting consistent texture, they hve difficulty filling holes in photogrphs of relworld scenes, which often consist of liner structures nd composite textures multiple textures intercting sptilly [23]. The min prolem is tht oundries etween imge regions re complex product of mutul influences etween different textures. In constrst to the twodimensionl nture of pure textures, these oundries form wht might e considered more onedimensionl, or liner, imge structures. A numer of lgorithms specificlly ddress this issue for the tsk of imge restortion, where speckles, scrtches, nd overlid text re removed [2, 3, 4, 7, 20]. These imge inpinting techniques fill holes in imges y propgting liner structures (clled isophotes in the inpinting literture) into the trget region vi diffusion. They re inspired y the prtil differentil equtions of physicl het flow, 1
2 nd work convincingly s restortion lgorithms. Their drwck is tht the diffusion process introduces some lur, which is noticele when the lgorithm is pplied to fill lrger regions. The lgorithm presented here comines the strengths of oth pproches. As with inpinting, we py specil ttention to liner structures. But, liner structures utting the trget region only influence the fill order of wht is t core n exemplrsed texture synthesis lgorithm. The result is n lgorithm tht hs the efficiency nd qulittive performnce of exemplrsed texture synthesis, ut which lso respects the imge constrints imposed y surrounding liner structures. Our lgorithm uilds on very recent reserch long similr lines. The work in [5] decomposes the originl imge into two components; one of which is processed y inpinting nd the other y texture synthesis. The output imge is the sum of the two processed components. This pproch still remins limited to the removl of smll imge gps, however, s the diffusion process continues to lur the filled region (cf., [5], fig.5 top right). The utomtic switching etween pure texture nd pure structuremode descried in [21] is lso voided. One of the first ttempts to use exemplrsed synthesis specificlly for oject removl ws y Hrrison [13]. There, the order in which pixel in the trget region is filled ws dictted y the level of texturedness of the pixel s neighorhood 1. Although the intuition is sound, strong liner structures were often overruled y nery noise, minimizing the vlue of the extr computtion. A relted technique drove the fill order y the locl shpe of the trget region, ut did not seek to explicitly propgte liner structure [6]. Finlly, Zlesny et l. [23] descrie n interesting lgorithm for the prllel synthesis of composite textures. They devise specilpurpose solution for the interfce etween two textures. In this pper we show tht, in fct, only one mechnism is sufficient for the synthesis of oth pure nd composite textures. Section 2 presents the key oservtion on which our lgorithm depends. Section 3 descries the detils of the lgorithm. Results on oth synthetic nd rel imgery re presented in section Exemplrsed synthesis suffices The core of our lgorithm is n isophotedriven imgesmpling process. It is wellunderstood tht exemplrsed pproches perform well for twodimensionl textures [1, 9, 15]. But, we note in ddition tht exemplrsed texture synthesis is sufficient for propgting extended liner imge structures, s well. A seprte synthesis 1 An implementtion of Hrrison s lgorithm is ville from pfh/resynthesizer/ Figure 2: Structure propgtion y exemplrsed texture synthesis. () Originl imge, with the trget region Ω, its contour δω nd the source region Φ clerly mrked. () We wnt to synthesize the re delimited y the ptch Ψ p centred on the point p δω. (c) The most likely cndidte mtches for Ψ p lie long the oundry etween the two textures in the source region, e.g., Ψ q nd Ψ q. (d) The est mtching ptch in the cndidtes set hs een copied into the position occupied y Ψ p, thus chieving prtil filling of Ω. The trget region Ω hs, now, shrnk nd its front hs ssumed different shpe. See text for detils. mechnism is not required for hndling isophotes. Figure 2 illustrtes this point. For ese of comprison, we dopt nottion similr to tht used in the inpinting literture. The region to e filled, i.e., the trget region is indicted y Ω, nd its contour is denoted δω. The contour evolves inwrd s the lgorithm progresses, nd so we lso refer to it s the fill front. The source region, Φ, which remins fixed throughout the lgorithm, provides smples used in the filling process. We now focus on single itertion of the lgorithm to show how structure nd texture re dequtely hndled y exemplrsed synthesis. Suppose tht the squre templte Ψ p Ω centred t the point p (fig. 2), is to e filled. The estmtch smple from the source region comes from the ptch Ψˆq Φ, which is most similr to those prts tht re lredy filled in Ψ p. In the exmple in fig. 2, we see tht if Ψ p lies on the continution of n imge edge, the most likely est mtches will lie long the sme (or similrly coloured) edge (e.g., Ψ q nd Ψ q in fig. 2c). All tht is required to propgte the isophote inwrds is simple trnsfer of the pttern from the estmtch source ptch (fig. 2d). Notice tht isophote orienttion is utomticlly preserved. In the figure, despite the fct tht the originl edge is not orthogonl to the trget contour δω, the propgted structure hs mintined the sme orienttion s in the source region. 3. Regionfilling lgorithm We now proceed with the detils of our lgorithm. First, user selects trget region, Ω, to e removed nd filled. The source region, Φ, my e defined s the entire imge minus the trget region (Φ = I Ω), s dilted nd round the trget region, or it my e mnully specified y the user. Next, s with ll exemplrsed texture synthesis [10], the size of the templte window Ψ must e specified. We provide defult window size of 9 9 pixels, ut in prctice require the user to set it to e slightly lrger thn the lrgest 2
3 Figure 3: Nottion digrm. Given the ptch Ψ p, n p is the norml to the contour δω of the trget region Ω nd Ip is the isophote (direction nd intensity) t point p. The entire imge is denoted with I. distinguishle texture element, or texel, in the source region. Once these prmeters re determined, the reminder of the regionfilling process is completely utomtic. In our lgorithm, ech pixel mintins colour vlue (or empty, if the pixel is unfilled) nd confidence vlue, which reflects our confidence in the pixel vlue, nd which is frozen once pixel hs een filled. During the course of the lgorithm, ptches long the fill front re lso given temporry priority vlue, which determines the order in which they re filled. Then, our lgorithm itertes the following three steps until ll pixels hve een filled: 1. Computing ptch priorities. Filling order is crucil to nonprmetric texture synthesis [1, 6, 10, 13]. Thus fr, the defult fvourite hs een the onion peel method, where the trget region is synthesized from the outside inwrd, in concentric lyers. To our knowledge, however, designing fill order which explicitly encourges propgtion of liner structure (together with texture) hs never een explored. Our lgorithm performs this tsk through estfirst filling lgorithm tht depends entirely on the priority vlues tht re ssigned to ech ptch on the fill front. The priority computtion is ised towrd those ptches which re on the continution of strong edges nd which re surrounded y highconfidence pixels. Given ptch Ψ p centred t the point p for some p δω (see fig. 3), its priority P (p) is defined s the product of two terms: P (p) = C(p)D(p). (1) We cll C(p) the confidence term nd D(p) the dt term, nd they re defined s follows: q Ψ C(p) = p Ω C(q), D(p) = I p n p Ψ p α where Ψ p is the re of Ψ p, α is normliztion fctor (e.g., α = 255 for typicl greylevel imge), nd n p is unit vector orthogonl to the front δω in the point p. The priority is computed for every order ptch, with distinct ptches for ech pixel on the oundry of the trget region. During initiliztion, the function C(p) is set to C(p) = 0 p Ω, nd C(p) = 1 p I Ω. The confidence term C(p) my e thought of s mesure of the mount of relile informtion surrounding the pixel p. The intention is to fill first those ptches which hve more of their pixels lredy filled, with dditionl preference given to pixels tht were filled erly on (or tht were never prt of the trget region). This utomticlly incorportes preference towrds certin shpes long the fill front. For exmple, ptches tht include corners nd thin tendrils of the trget region will tend to e filled first, s they re surrounded y more pixels from the originl imge. These ptches provide more relile informtion ginst which to mtch. Conversely, ptches t the tip of peninsuls of filled pixels jutting into the trget region will tend to e set side until more of the surrounding pixels re filled in. At corse level, the term C(p) of (1) pproximtely enforces the desirle concentric fill order. As filling proceeds, pixels in the outer lyers of the trget region will tend to e chrcterized y greter confidence vlues, nd therefore e filled erlier; pixels in the centre of the trget region will hve lesser confidence vlues. The dt term D(p) is function of the strength of isophotes hitting the front δω t ech itertion. This term oosts the priority of ptch tht n isophote flows into. This fctor is of fundmentl importnce in our lgorithm ecuse it encourges liner structures to e synthesized first, nd, therefore propgted securely into the trget region. Broken lines tend to connect, thus relizing the Connectivity Principle of vision psychology [7, 17] (cf., fig. 4, fig. 7d, fig. 8 nd fig. 13d). There is delicte lnce etween the confidence nd dt terms. The dt term tends to push isophotes rpidly inwrd, while the confidence term tends to suppress precisely this sort of incursion into the trget region. As presented in the results section, this lnce is hndled grcefully vi the mechnism of single priority computtion for ll ptches on the fill front. Since the fill order of the trget region is dictted solely y the priority function P (p), we void hving to predefine n ritrry fill order s done in existing ptchsed pproches [9, 19]. Our fill order is function of imge properties, resulting in n orgnic synthesis process tht elimintes the risk of rokenstructure rtefcts (fig. 7c) nd lso reduces locky rtefcts without n expensive ptchcutting step [9] or lurinducing lending step [19]. 2. Propgting texture nd structure informtion. Once ll priorities on the fill front hve een computed, the ptch Ψˆp with highest priority is found. We then fill it with dt extrcted from the source region Φ. In trditionl inpinting techniques, pixelvlue inform 3
4 c d e f g Figure 4: Reliztion of the Connectivity Principle [7, 17] on synthetic exmple. () Originl imge, the Knizs tringle with rndom noise dded. () The occluding white tringle in the originl imge hs een mnully selected s the trget region (24% of totl imge re) nd mrked in red. (c...f) Different stges of the filling process. (d) Notice tht strong edges re pushed inside the trget region first nd tht shrp ppendices (e.g., the vertices of the red tringle) re rpidly smoothed. (f) When no structures hit the front δω the trget region evolves in roughly circulr shpe. (g) The output imge where the trget region hs een filled, i.e., the occluding tringle removed. Little imperfections re present in the curvture of the circles in the reconstructed res, while the sides of the internl tringle hve een correctly connected. The lur typicl of diffusion techniques is completely voided. See figs. 7, 8, 13 for further exmples of structurl continution. tion is propgted vi diffusion. As noted previously, diffusion necessrily leds to imge smoothing, which results in lurry fillin, especilly of lrge regions (see fig. 10f). On the contrry, we propgte imge texture y direct smpling of the source region. Similr to [10], we serch in the source region for tht ptch which is most similr to Ψˆp. 2 Formlly, Ψˆq = rg min Ψ q Φ d(ψˆp, Ψ q ) (2) where the distnce d(ψ, Ψ ) etween two generic ptches Ψ nd Ψ is simply defined s the sum of squred differences (SSD) of the lredy filled pixels in the two ptches. We use the CIE L colour spce ecuse of its property of perceptul uniformity [18]. Hving found the source exemplr Ψˆq, the vlue of ech pixeltoefilled, p p Ψˆp Ω, is copied from its corresponding position inside Ψˆq. This suffices to chieve the propgtion of oth structure nd texture informtion from the source Φ to the trget region Ω, one ptch t time (cf., fig. 2d). In fct, we note tht ny further mnipultion of the pixel vlues (e.g., dding noise, smoothing nd so forth) tht does not explicitly depend upon sttistics of the source region, is fr more likely to degrde visul similrity etween the filled region nd the source region, thn to improve it. 3. Updting confidence vlues. After the ptch Ψˆp hs een filled with new pixel vlues, the confidence C(p) is updted in the re delimited y Ψˆp s follows: C(q) = C(ˆp) q Ψˆp Ω. This simple updte rule llows us to mesure the reltive confidence of ptches on the fill front, without imgespecific prmeters. As filling proceeds, confidence vlues decy, indicting tht we re less sure of the colour vlues of pixels ner the centre of the trget region. 2 Vlid ptches Ψˆq must e entirely contined in Φ. Extrct the mnully selected initil front δω 0. Repet until done: 1. Identify the fill front δω t. If Ω t =, exit. 1. Compute priorities P (p) p δω t. 2. Find the ptch Ψˆp with the mximum priority, i.e., Ψˆp ˆp = rg mx p δω t P (p) 2. Find the exemplr Ψˆq Φ tht minimizes d(ψˆp, Ψˆq ). 2c. Copy imge dt from Ψˆq to Ψˆp. 3. Updte C(p) p p Ψˆp Ω Tle 1: Region filling lgorithm. A pseudocode description of the lgorithmic steps is shown in tle 1. The superscript t indictes the current itertion. 4. Results nd comprisons Here we pply our lgorithm to vriety of imges, rnging from purely synthetic imges to fullcolour photogrphs tht include complex textures. Where possile, we mke sideyside comprisons to previously proposed methods. In other cses, we hope the reder will refer to the originl source of our test imges (mny re tken from previous literture on inpinting nd texture synthesis) nd compre these results with the results of erlier work. In ll of the experiments, the ptch size ws set to e greter thn the lrgest texel or the thickest structure (e.g., edges) in the source region. Furthermore, unless otherwise stted the source region hs een set to e Φ = I Ω. All experiments were run on 2.5GHz Pentium IV with 1GB of RAM. The Knizs tringle. We perform our first experiment on the wellknown Knizs tringle [17] to show how the lgorithm works on structurerich synthetic imge. As shown in fig. 4, our lgorithm deforms the fill front δω under the ction of two forces: isophote continution (the dt term, D(p)) nd the pressure from surrounding filled pixels (the confidence term, C(p)). 4
5 c d Figure 7: Onion peel vs. structureguided filling. () Originl imge. () The trget region hs een selected nd mrked in red. (c) Results of filling y concentric lyers. (d) Results of filling with our lgorithm. Thnks to the dt term in (1) the pole is reconstructed correctly. The shrp liner structures of the incomplete green tringle re grown into the trget region. But lso, no single structurl element domintes ll of the others; this lnce mong competing isophotes is chieved through the nturlly decying confidence vlues (in n erlier version of our lgorithm which lcked this lnce, runwy structures led to lrgescle rtefcts.) Figures 4e,f lso show the effect of the confidence term in smoothing shrp ppendices such s the vertices of the trget region (in red). As descried ove, the confidence is propgted in mnner similr to the frontpropgtion lgorithms used in inpinting. We stress, however, tht unlike inpinting, it is the confidence vlues tht re propgted long the front (nd which determine fill order), not colour vlues themselves, which re smpled from the source region. Finlly, we note tht despite the lrge size of the removed region, edges nd lines in the filled region re s shrp s ny found in the source region. There is no lurring from diffusion processes. This is property of exemplrsed texture synthesis. The effect of different filling strtegies. Figures 5, 6 nd 7 demonstrte the effect of different filling strtegies. Figure 5f shows how our filling lgorithm chieves the est structurl continution in simple, synthetic imge. Figure 6 further demonstrtes the vlidity of our lgorithm on n eril photogrph. The pixel trget region hs een selected to strddle two different textures (fig. 6). The reminder of the imge in fig. 6 ws used s source for ll the experiments in fig. 6. With rsterscn synthesis (fig. 6c) not only does the top region (the river) grow into the ottom one (the city re), ut visile sems lso pper t the ottom of the trget region. This prolem is only prtilly ddressed y concentric filling (fig 6d). Similrly, in fig. 6e the sophisticted ordering proposed y Hrrison [13] only modertely succeeds in preventing this phenomenon. In ll of these cses, the primry difficulty is tht since the (eventul) texture oundry is the most constrined prt Figure 8: Comprison with trditionl structure inpinting. () Originl imge. () Oject removl nd structure recovery vi our lgorithm; to e compred with fig.4 in [4]. of the trget region, it should e filled first. But, unless this is explicitly ddressed in determining the fill order, the texture oundry is often the lst prt to e filled. The lgorithm proposed in this pper is designed to ddress this prolem, nd thus more nturlly extends the contour etween the two textures s well s the verticl grey rod. In the exmple in fig. 6, our lgorithm fills the trget region in only 2 seconds, on Pentium IV, 2.52GHz, 1GB RAM. Hrrison s resynthesizer [13], which is the nerest in qulity, requires pproximtely 45 seconds. Figure 7 shows yet nother comprison etween the concentric filling strtegy nd the proposed lgorithm. In the presence of concve trget regions, the onion peel filling my led to visile rtefcts such s unrelisticlly roken structures (see the pole in fig. 7c). Conversely, the presence of the dt term of (1) encourges the edges of the pole to grow first inside the trget region nd thus correctly reconstruct the complete pole (fig. 7d). This exmple demonstrtes the roustness of the proposed lgorithm with respect to the shpe of the selected trget region. Comprisons with inpinting. We now turn to some exmples from the inpinting literture. The first two exmples show tht our pproch works t lest s well s inpinting. The first (fig. 8) is synthesized imge of two ellipses [4]. The occluding white torus is removed from the input imge nd two drk ckground ellipses reconstructed vi our lgorithm (fig. 8). This exmple ws chosen y uthors of the originl work on inpinting to illustrte the structure propgtion cpilities of their lgorithm. Our results re visully identicl to those otined y inpinting ([4], fig.4). We now compre results of the restortion of n hnddrwn imge. In fig. 9 the im is to remove the foreground text. Our results (fig. 9) re mostly indistinguishle with those otined y trditionl inpinting 3. This exmple demonstrtes the effectiveness of oth techniques in imge restortion pplictions. It is in rel photogrphs with lrge ojects to remove, 3 5
6 c d e f Figure 5: Effect of filling order on synthetic imge. () The originl imge; () The trget region hs een selected nd mrked in lck; (c) Filling the trget region in rsterscn order; (d) Filling y concentric lyers; (e) The result of pplying Hrrison s technique which took 2 45 ; (f) Filling with our lgorithm which took 5. Notice tht even though the tringle upper vertex is not complete our technique performs etter thn the others. c d e f Figure 6: Effect of filling order on n eril photogrph. () The originl imge, n eril view of London. () The trget region hs een selected nd mrked in red; Notice tht it strddles two different textures; (c) Filling with rsterscn order; (d) Filling y concentric lyers; (e) The result of pplying Hrrison s technique (performed in 45 ); (f) Filling with our lgorithm (performed in 2 ). See text for detils. Figure 11 compres our lgorithm to the recent texture nd structure inpinting technique descried in [5]. Figure 11(ottom right) shows tht lso our lgorithm ccomplishes the propgtion of structure nd texture inside the selected trget region. Moreover, the lck of diffusion steps voids lurring propgted structures (see the verticl edge in the encircled region) nd mkes the lgorithm more computtionlly efficient. Figure 9: Imge restortion exmple. () Originl imge. The text occupies 9% of the totl imge re. () Result of text removl vi our lgorithm. however, tht the rel dvntges of our pproch ecome pprent. Figure 10 shows n exmple on rel photogrph, of ungee jumper in midjump (from [4], fig.8). In the originl work, the thin ungee cord is removed from the imge vi inpinting. In order to prove the cpilities of our lgorithm we removed the entire ungee jumper (fig. 10e). Structures such s the shore line nd the edge of the house hve een utomticlly propgted into the trget region long with plusile textures of shruery, wter nd roof tiles; nd ll this with no priori model of nything specific to this imge. For comprison, figure 10f shows the result of filling the sme trget region (fig. 10) y imge inpinting 4. Considerle lur is introduced into the trget region ecuse of inpinting s use of diffusion to propgte colour vlues; nd highfrequency texturl informtion is entirely sent ,000 itertions were run using the implementtion in j/inpinting/ Synthesizing composite textures. Fig. 12 demonstrtes tht our lgorithm ehves well lso t the oundry etween two different textures, such s the ones nlyzed in [23]. The trget region selected in fig. 12c strddles two different textures. The qulity of the knitting in the contour reconstructed vi our pproch (fig. 12d) is similr to the originl imge nd to the results otined in the originl work (fig. 12), ut gin, this hs een ccomplished without complicted texture models or seprte oundryspecific texture synthesis lgorithm. Further exmples on photogrphs. We show two more exmples on photogrphs of rel scenes. Figure 13 demonstrtes, gin, the dvntge of the proposed pproch in preventing structurl rtefcts (cf., 7d). While the onionpeel pproch produces deformed horizon, our lgorithm reconstructs the oundry etween sky nd se s convincing stright line. Finlly, in fig. 14, the foreground person hs een mnully selected nd the corresponding region filled in utomticlly. The filled region in the output imge convincingly mimics the complex ckground texture with no prominent rtefcts (fig. 14f). During the filling process the topologicl chnges of the trget region re hndled effortlessly. 6
7 c d e f Figure 10: Removing lrge ojects from photogrphs. () Originl imge (from [4]), pix. () The trget region (in white) covers 12% of the totl imge re. (c,d) Different stges of the filling process. Notice how the isophotes hitting the oundry of the trget region re propgted inwrds while thin ppendices (e.g., the rms) in the trget region tend to dispper quickly. (e) The finl imge where the ungee jumper hs een completely removed nd the occluded region reconstructed y our utomtic lgorithm (performed in 18, to e compred with 10 of Hrrison s resynthesizer). (f) The result of region filling y trditionl imge inpinting. Notice the lur introduced y the diffusion process nd the complete lck of texture in the synthesized re. 5. Conclusion nd future work This pper hs presented novel lgorithm for removing lrge ojects from digitl photogrphs. The result of oject removl is n imge in which the selected oject hs een replced y visully plusile ckground tht mimics the ppernce of the source region. Our pproch employs n exemplrsed texture synthesis technique modulted y unified scheme for determining the fill order of the trget region. Pixels mintin confidence vlue, which together with imge isophotes, influence their fill priority. The technique is cple of propgting oth liner structure nd twodimensionl texture into the trget region. Comprtive experiments show tht creful selection of the fill order is necessry nd sufficient to hndle this tsk. Our method performs t lest s well s previous techniques designed for the restortion of smll scrtches, nd in instnces in which lrger ojects re removed, it drmticlly outperforms erlier work in terms of oth perceptul qulity nd computtionl efficiency. Currently, we re investigting extensions for more ccurte propgtion of curved structures in still photogrphs nd for oject removl from video, which promise to impose n entirely new set of chllenges. Acknowledgements. The uthors would like to thnk M. Gngnet, A. Blke nd P. Anndn for inspiring discussions; nd G. Spiro, M. Bertlmio, L. vn Gool nd A. Zlesny for mking some of their imges ville. References [1] M. Ashikhmin. Synthesizing nturl textures. In Proc. ACM Symp. on Interctive 3D Grphics, pp , Reserch Tringle Prk, NC, Mr [2] C. Bllester, V. Cselles, J. Verder, M. Bertlmio, nd G. Spiro. A vritionl model for fillingin gry level nd color imges. In Proc. ICCV, pp. I: 10 16, Vncouver, Cnd, Jun Figure 11: Comprison with texture nd structure inpinting. (Top) Originl imge (from [5]). The trget regions re mrked in white. (Bottom left) Region filling vi the inpinting lgorithm in [5]. Notice the lur of the edge in the circled region. (Bottom right) The result of our lgorithm. Both structure nd texture hve een nicely propgted inside the trget region. The edge in the circled region is noticely shrper. [3] M. Bertlmio, A.L. Bertozzi, nd G. Spiro. Nvierstokes, fluid dynmics, nd imge nd video inpinting. In Proc. Conf. Comp. Vision Pttern Rec., pp. I: , Hwi, Dec [4] M. Bertlmio, G. Spiro, V. Cselles, nd C. Bllester. Imge inpinting. In Proc. ACM Conf. Comp. Grphics (SIGGRAPH), pp , New Orlens, LU, Jul guille/inpinting.htm. [5] M. Bertlmio, L. Vese, G. Spiro, nd S. Osher. Simultneous structure nd texture imge inpinting. to pper, guille/inpinting.htm. [6] R. Bornrd, E. Lecn, L. Lorelli, nd JH. Chenot. Missing dt correction in still imges nd imge sequences. In ACM Multimedi, Frnce, Dec [7] T. F. Chn nd J. Shen. Nontexture inpinting y curvturedriven diffusions (CDD). J. Visul Comm. Imge Rep., 4(12), [8] J.S. de Bonet. Multiresolution smpling procedure for nlysis nd synthesis of texture imges. In Proc. ACM Conf. Comp. Grphics (SIGGRAPH), volume 31, pp , [9] A. Efros nd W.T. Freemn. Imge quilting for texture synthesis nd trnsfer. In Proc. ACM Conf. Comp. Grphics (SIGGRAPH), pp , Eugene Fiume, Aug
8 c Figure 12: Comprison with prllel composite texture synthesis. () Originl imge, the fur of zer (from [23]). () The result of the synthesis lgorithm descried in [23]. (c) Originl imge with the trget region mrked in red (22% of totl imge size). (d) The trget region hs een filled vi our lgorithm. The knitting effect long the oundry etween the two textures is correctly reproduced y our technique. [10] A. Efros nd T. Leung. Texture synthesis y nonprmetric smpling. In Proc. ICCV, pp , Kerkyr, Greece, Sep [11] W.T. Freemn, E.C. Psztor, nd O.T. Crmichel. Lerning lowlevel vision. Int. J. Computer Vision, 40(1):25 47, [12] D. Grer. Computtionl Models for Texture Anlysis nd Texture Synthesis. PhD thesis, Univ. of Southern Cliforni, USA, [13] P. Hrrison. A nonhierrchicl procedure for resynthesis of complex texture. In Proc. Int. Conf. Centrl Europe Comp. Grphics, Visu. nd Comp. Vision, Plzen, Czech Repulic, Fe [14] D.J. Heeger nd J.R. Bergen. Pyrmidsed texture nlysis/synthesis. In Proc. ACM Conf. Comp. Grphics (SIGGRAPH), volume 29, pp , Los Angeles, CA, [15] A. Hertzmnn, C. Jcos, N. Oliver, B. Curless, nd D. Slesin. Imge nlogies. In Proc. ACM Conf. Comp. Grphics (SIGGRAPH), Eugene Fiume, Aug [16] H. Igehy nd L. Pereir. Imge replcement through texture synthesis. In Proc. Int. Conf. Imge Processing, pp. III: , [17] G. Knizs. Orgniztion in Vision. Preger, New York, [18] J. M. Ksson nd W. Plouffe. An nlysis of selected computer interchnge color spces. In ACM Trnsctions on Grphics, volume 11, pp , Oct [19] L. Ling, C. Liu, Y.Q. Xu, B. Guo, nd H.Y. Shum. Reltime texture synthesis y ptchsed smpling. In ACM Trnsctions on Grphics, [20] S. Msnou nd J.M. Morel. Level lines sed disocclusion. In Int. Conf. Imge Processing, Chicgo, [21] S. Rne, G. Spiro, nd M. Bertlmio. Structure nd texture fillingin of missing imge locks in wireless trnsmission nd compression pplictions. In IEEE. Trns. Imge Processing, to pper. [22] L.W. Wey nd M. Levoy. Fst texture synthesis using treestructured vector quntiztion. In Proc. ACM Conf. Comp. Grphics (SIGGRAPH), [23] A. Zlesny, V. Ferrri, G. Cenen, nd L. vn Gool. Prllel composite texture synthesis. In Texture 2002 workshop  (in conjunction with ECCV02), Copenhgen, Denmrk, Jun d c Figure 13: Concentriclyer filling vs. the proposed guided filling lgorithm. () Originl imge. () The mnully selected trget region (20% of the totl imge re, in red). (c) The result of filling y concentric lyers. The deformtion of the horizon is cused y the fct tht in the concentric lyers filling sky nd se grow inwrds t the sme speed. Thus, the reconstructed skyse oundry tends to follow the skeleton of the selected trget region. (d) The result of filling y the proposed lgorithm. The horizon is correctly reconstructed s stright line. c e Figure 14: Removing lrge ojects from photogrphs. () Originl imge. () The trget region (10% of the totl imge re) hs een lnked out. (c...e) Intermedite stges of the filling process. (f) The trget region hs een completely filled nd the selected oject removed. The source region hs een utomticlly selected s nd round the trget region. The edges of the stones hve een nicely propgted inside the trget region together with the wter texture. d d f 8
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