Jia, J.Y.[Jia-Ya],
Tang, C.K.[Chi-Keung],
Inference of Segmented Color and Texture Description by Tensor Voting,
PAMI(26), No. 6, June 2004, pp. 771-786.
IEEE Abstract.
0404
BibRef
Earlier:
Image repairing: robust image synthesis by adaptive ND tensor voting,
CVPR03(I: 643-650).
IEEE DOI
0307
Texture Synthesis. Automatically infer missing regions by a ND tensor voting method.
See also Tensor Voting for Image Correction by Global and Local Intensity Alignment.
BibRef
Fang, C.W.[Chih-Wei],
Lien, J.J.J.[Jenn-Jier James],
Rapid Image Completion System Using Multiresolution Patch-Based
Directional and Nondirectional Approaches,
IP(18), No. 12, December 2009, pp. 2769-2779.
IEEE DOI
0912
BibRef
Earlier:
Fast Directional Image Completion,
PSIVT07(48-61).
Springer DOI
0712
BibRef
Earlier:
Fast Image Replacement Using Multi-resolution Approach,
ACCV06(II:509-520).
Springer DOI
0601
Texture inpainting.
BibRef
Xu, Z.,
Sun, J.,
Image Inpainting by Patch Propagation Using Patch Sparsity,
IP(19), No. 5, May 2010, pp. 1153-1165.
IEEE DOI
1004
BibRef
Liu, D.[Dong],
Sun, X.Y.[Xiao-Yan],
Wu, F.[Feng],
Inpainting with image patches for compression,
JVCIR(23), No. 1, January 2012, pp. 100-113.
Elsevier DOI
1112
Assistant information; Displacement vector; Image compression; Image
patches; Inpainting; Intra prediction; Non-parametric; Rate-distortion
optimization; Texture synthesis
BibRef
Shi, Z.B.[Zhong-Bo],
Sun, X.Y.[Xiao-Yan],
Wu, F.[Feng],
Multi-model prediction for image set compression,
VCIP13(1-6)
IEEE DOI
1402
correlation methods
BibRef
Ram, I.,
Elad, M.,
Cohen, I.,
Image Processing Using Smooth Ordering of its Patches,
IP(22), No. 7, 2013, pp. 2764-2774.
IEEE DOI
1307
smoothing methods; corrupted image; inpainting;
patch-based processing
BibRef
Ram, I.,
Cohen, I.,
Elad, M.[Michael],
Patch-Ordering-Based Wavelet Frame and Its Use in Inverse Problems,
IP(23), No. 7, July 2014, pp. 2779-2792.
IEEE DOI
1407
Approximation algorithms
BibRef
Ram, I.,
Cohen, I.,
Elad, M.[Michael],
Facial Image Compression using Patch-Ordering-Based Adaptive Wavelet
Transform,
SPLetters(21), No. 10, October 2014, pp. 1270-1274.
IEEE DOI
1407
Dictionaries
BibRef
Ulen, J.,
Strandmark, P.[Petter],
Kahl, F.[Fredrik],
An Efficient Optimization Framework for Multi-Region Segmentation Based
on Lagrangian Duality,
MedImg(32), No. 2, February 2013, pp. 178-188.
IEEE DOI
1301
BibRef
Earlier: A2, A3, Only:
Curvature Regularization for Curves and Surfaces in a Global
Optimization Framework,
EMMCVPR11(205-218).
Springer DOI
1107
BibRef
Schoenemann, T.[Thomas],
Kuang, Y.B.[Yu-Bin],
Kahl, F.[Fredrik],
Curvature Regularity for Multi-label Problems:
Standard and Customized Linear Programming,
EMMCVPR11(163-176).
Springer DOI
1107
BibRef
Schoenemann, T.[Thomas],
Kahl, F.[Fredrik],
Masnou, S.[Simon],
Cremers, D.[Daniel],
A Linear Framework for Region-Based Image Segmentation and Inpainting
Involving Curvature Penalization,
IJCV(99), No. 1, August 2012, pp. 53-68.
WWW Link.
1202
BibRef
Earlier: A1, A2, A4, Only:
Curvature regularity for region-based image segmentation and
inpainting: A linear programming relaxation,
ICCV09(17-23).
IEEE DOI
0909
BibRef
Chang, I.C.[I-Cheng],
Yu, J.C.[J. Cloud],
Chang, C.C.[Chih-Chuan],
A forgery detection algorithm for exemplar-based inpainting images
using multi-region relation,
IVC(31), No. 1, January 2013, pp. 57-71.
Elsevier DOI
1302
Digital image forensics; Digital forgery detection; Exemplar-based
inpainting; Multi-region relation; Weight transformation
BibRef
Vaksman, G.[Gregory],
Zibulevsky, M.[Michael],
Elad, M.[Michael],
Patch Ordering as a Regularization for Inverse Problems in Image
Processing,
SIIMS(9), No. 1, 2016, pp. 287-319.
DOI Link
1604
BibRef
Kim, B.[Baeksop],
Kim, J.[Jiseong],
So, J.M.[Jung-Min],
Pixel and Patch Reordering for Fast Patch Selection in Exemplar-Based
Image Inpainting,
IEICE(E96-D), No. 12, December 2013, pp. 2892-2895.
WWW Link.
1312
BibRef
So, J.M.[Jung-Min],
Kim, B.[Baeksop],
A Fast Exemplar-Based Image Inpainting Method Using Bounding Based on
Mean and Standard Deviation of Patch Pixels,
IEICE(E98-D), No. 8, August 2015, pp. 1553-1561.
WWW Link.
1509
BibRef
He, K.M.[Kai-Ming],
Sun, J.[Jian],
Image Completion Approaches Using the Statistics of Similar Patches,
PAMI(36), No. 12, December 2014, pp. 2423-2435.
IEEE DOI
1411
BibRef
Earlier:
Statistics of Patch Offsets for Image Completion,
ECCV12(II: 16-29).
Springer DOI
1210
Cost function
BibRef
Li, Z.D.[Zhi-Dan],
He, H.J.[Hong-Jie],
Tai, H.M.[Heng-Ming],
Yin, Z.K.[Zhong-Ke],
Chen, F.[Fan],
Color-Direction Patch-Sparsity-Based Image Inpainting Using
Multidirection Features,
IP(24), No. 3, March 2015, pp. 1138-1152.
IEEE DOI
1502
Coherence
BibRef
Chen, Z.H.[Zhi-Hua],
Dai, C.[Chao],
Jiang, L.[Lei],
Sheng, B.[Bin],
Zhang, J.[Jing],
Lin, W.Y.[Wei-Yao],
Yuan, Y.[Yubo],
Structure-aware image inpainting using patch scale optimization,
JVCIR(40, Part A), No. 1, 2016, pp. 312-323.
Elsevier DOI
1609
Image inpainting
BibRef
Zhu, X.S.[Xin-Shan],
Qian, Y.J.[Yong-Jun],
Zhao, X.F.[Xian-Feng],
Sun, B.[Biao],
Sun, Y.[Ya],
A deep learning approach to patch-based image inpainting forensics,
SP:IC(67), 2018, pp. 90-99.
Elsevier DOI
1808
Inpainting, Forensics, Convolutional neural network,
Loss function, Compression
BibRef
Wu, W.[Wei],
Ge, L.[Luoqi],
Luo, J.C.[Jian-Cheng],
Huan, R.H.[Ruo-Hong],
Yang, Y.P.[Ying-Pin],
A Spectral-Temporal Patch-Based Missing Area Reconstruction for
Time-Series Images,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Liu, J.,
Yang, S.,
Fang, Y.,
Guo, Z.,
Structure-Guided Image Inpainting Using Homography Transformation,
MultMed(20), No. 12, December 2018, pp. 3252-3265.
IEEE DOI
1812
computational complexity, image restoration, image segmentation,
image texture, Markov processes, statistical analysis,
image completion
BibRef
Liu, B.[Bowen],
Li, P.[Ping],
Sheng, B.[Bin],
Nie, Y.W.[Yong-Wei],
Wu, E.[Enhua],
Structure-preserving image completion with multi-level dynamic patches,
VC(35), No. 1, January 2018, pp. 85-98.
WWW Link.
1902
BibRef
Hu, W.J.[Wen-Jin],
Ye, Y.Q.[Yu-Qi],
Zeng, F.L.[Fu-Liang],
Meng, J.H.[Jia-Hao],
A new method of Thangka image inpainting quality assessment,
JVCIR(59), 2019, pp. 292-299.
Elsevier DOI
1903
Thangka image: Buddist religious art.
Image inpainting quality assessment, Structural symmetry,
Thangka image, Harris corner
BibRef
Yang, X.H.[Xiu-Hong],
Guo, B.L.[Bao-Long],
Xiao, Z.L.[Zhao-Lin],
Liang, W.[Wei],
Improved structure tensor for fine-grained texture inpainting,
SP:IC(73), 2019, pp. 84-95.
Elsevier DOI
1904
Texture, Inpainting, Structure tensor, Non-local, Fractional-order
BibRef
Jin, D.,
Bai, X.,
Patch-Sparsity-Based Image Inpainting Through a Facet Deduced
Directional Derivative,
CirSysVideo(29), No. 5, May 2019, pp. 1310-1324.
IEEE DOI
1905
Image restoration, Gray-scale, Mathematical model,
Prediction algorithms, Dictionaries, Filling, Image inpainting,
sparse representation
BibRef
Cao, Z.Y.[Zhi-Yi],
Niu, S.Z.[Shao-Zhang],
Zhang, J.W.[Ji-Wei],
Wang, X.Y.[Xin-Yi],
Fast generative adversarial networks model for masked image restoration,
IET-IPR(13), No. 7, 30 May 2019, pp. 1124-1129.
DOI Link
1906
Masked area, for large masked area.
BibRef
Helbert, D.[David],
Malek, M.[Mohamed],
Bourdon, P.[Pascal],
Carré, P.[Philippe],
Patch graph-based wavelet inpainting for color images,
JVCIR(64), 2019, pp. 102614.
Elsevier DOI
1911
Image processing, Inpainting, Color image
BibRef
Shao, H.[Hang],
Wang, Y.X.[Yong-Xiong],
Fu, Y.H.[Ying-Hua],
Yin, Z.[Zhong],
Generative image inpainting via edge structure and color aware fusion,
SP:IC(87), 2020, pp. 115929.
Elsevier DOI
2007
Deep learning, Image inpainting,
Generative adversarial network, Content aware fill, Multi-map fusion
BibRef
Paulino, I.R.[Ignacio Ramírez],
Hounie, I.[Ignacio],
Image Inpainting using Patch Consensus and DCT Priors,
IPOL(11), 2021, pp. 1-17.
DOI Link
2101
More information on project:
WWW Link.
BibRef
Paulino, I.R.[Ignacio Ramírez],
PACO: Global Signal Restoration via PAtch COnsensus,
ArXive-prints, 2018.
WWW Link.
BibRef
1800
Wang, N.,
Zhang, Y.,
Zhang, L.,
Dynamic Selection Network for Image Inpainting,
IP(30), 2021, pp. 1784-1798.
IEEE DOI
2101
Convolution, Task analysis, Standards, Feature extraction, Semantics,
Logic gates, Training, Image inpainting, deep learning, dynamic selection
BibRef
Nortje, A.[André],
Brink, W.[Willie],
Engelbrecht, H.A.[Herman A.],
Kamper, H.[Herman],
BINet: A binary inpainting network for deep patch-based image
compression,
SP:IC(92), 2021, pp. 116119.
Elsevier DOI
2101
Image compression, Image inpainting,
Image representation coding, Deep compression
BibRef
Chen, M.[Minyu],
Liu, Z.[Zhi],
EDBGAN: Image Inpainting via an Edge-Aware Dual Branch Generative
Adversarial Network,
SPLetters(28), 2021, pp. 842-846.
IEEE DOI
2106
Feature extraction, Image edge detection, Convolution, Generators,
Task analysis, Logic gates, Generative adversarial networks,
image inpainting
BibRef
Xu, R.[Rui],
Guo, M.H.[Ming-Hao],
Wang, J.Q.[Jia-Qi],
Li, X.X.[Xiao-Xiao],
Zhou, B.[Bolei],
Loy, C.C.[Chen Change],
Texture Memory-Augmented Deep Patch-Based Image Inpainting,
IP(30), 2021, pp. 9112-9124.
IEEE DOI
2112
Image reconstruction, Image restoration, Training, Optimization,
Interpolation, Generative adversarial networks, Semantics,
texture synthesis
BibRef
Xu, T.[Ting],
Huang, T.Z.[Ting-Zhu],
Deng, L.J.[Liang-Jian],
Zhao, X.L.[Xi-Le],
Hu, J.F.[Jin-Fan],
Exemplar-based image inpainting using adaptive two-stage
structure-tensor based priority function and nonlocal filtering,
JVCIR(83), 2022, pp. 103430.
Elsevier DOI
2202
Exemplar-based image inpainting, Non-local texture matching,
Structure-tensor, Texture and structure synthesis,
Remote sensing image inpainting
BibRef
Wang, C.[Chao],
Shao, M.[Mingwen],
Meng, D.Y.[De-Yu],
Zuo, W.M.[Wang-Meng],
Dual-Pyramidal Image Inpainting With Dynamic Normalization,
CirSysVideo(32), No. 9, September 2022, pp. 5975-5988.
IEEE DOI
2209
Feature extraction, Convolution, Kernel, Task analysis, Semantics,
Representation learning, Frequency modulation, Deep learning,
task analysis
BibRef
Yan, Z.Y.[Zhao-Yi],
Li, X.M.[Xiao-Ming],
Li, M.[Mu],
Zuo, W.M.[Wang-Meng],
Shan, S.G.[Shi-Guang],
Shift-Net: Image Inpainting via Deep Feature Rearrangement,
ECCV18(XIV: 3-19).
Springer DOI
1810
BibRef
Liu, G.L.[Gui-Lin],
Dundar, A.[Aysegul],
Shih, K.J.[Kevin J.],
Wang, T.C.[Ting-Chun],
Reda, F.A.[Fitsum A.],
Sapra, K.[Karan],
Yu, Z.D.[Zhi-Ding],
Yang, X.D.[Xiao-Dong],
Tao, A.[Andrew],
Catanzaro, B.[Bryan],
Partial Convolution for Padding, Inpainting, and Image Synthesis,
PAMI(45), No. 5, May 2023, pp. 6096-6110.
IEEE DOI
2304
Convolution, Task analysis, Image synthesis, Semantics,
Feature extraction, Image edge detection, Visualization,
semantic segmentation
BibRef
Liu, G.L.[Gui-Lin],
Reda, F.A.[Fitsum A.],
Shih, K.J.[Kevin J.],
Wang, T.C.[Ting-Chun],
Tao, A.[Andrew],
Catanzaro, B.[Bryan],
Image Inpainting for Irregular Holes Using Partial Convolutions,
ECCV18(XI: 89-105).
Springer DOI
1810
BibRef
Zhang, R.S.[Rui-Song],
Quan, W.[Weize],
Zhang, Y.[Yong],
Wang, J.[Jue],
Yan, D.M.[Dong-Ming],
W-Net: Structure and Texture Interaction for Image Inpainting,
MultMed(25), 2023, pp. 7299-7310.
IEEE DOI
2311
BibRef
Li, Z.[Zhan],
Zhang, Y.[Yanan],
Du, Y.F.[Ying-Fei],
Wang, X.F.[Xiao-Feng],
Wen, C.[Chao],
Zhang, Y.Q.[Yong-Qin],
Geng, G.H.[Guo-Hua],
Jia, F.[Fan],
STNet: Structure and Texture-Guided Network for Image Inpainting,
PR(156), 2024, pp. 110786.
Elsevier DOI Code:
WWW Link.
2408
Image inpainting, Generative adversarial network,
Structure and texture, Ancient murals
BibRef
Chen, S.[Shuang],
Atapour-Abarghouei, A.[Amir],
Shum, H.P.H.[Hubert P. H.],
HINT: High-Quality INpainting Transformer With Mask-Aware Encoding
and Enhanced Attention,
MultMed(26), 2024, pp. 7649-7660.
IEEE DOI
2405
Transformers, Feature extraction, Image reconstruction,
Computational modeling, Task analysis, Data mining, representation learning
BibRef
Cai, J.F.[Jian-Feng],
Choi, J.K.[Jae Kyu],
Li, J.Y.[Jing-Yang],
Yin, G.J.[Guo-Jian],
Restoration Guarantee of Image Inpainting via Low Rank Patch Matrix
Completion,
SIIMS(17), No. 3, 2024, pp. 1879-1908.
DOI Link
2501
BibRef
Yamashita, Y.[Yohei],
Shimosato, K.[Kodai],
Ukita, N.[Norimichi],
Boundary-aware Image Inpainting with Multiple Auxiliary Cues,
NTIRE22(618-628)
IEEE DOI
2210
Image segmentation, Image edge detection, Estimation,
Image restoration
BibRef
Lu, Z.[Zeyu],
Jiang, J.J.[Jun-Jun],
Huang, J.Q.[Jun-Qin],
Wu, G.[Gang],
Liu, X.M.[Xian-Ming],
GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting,
NTIRE22(1300-1309)
IEEE DOI
2210
Training, Frequency-domain analysis,
Robustness
BibRef
Ke, L.[Lei],
Tai, Y.W.[Yu-Wing],
Tang, C.K.[Chi-Keung],
Occlusion-Aware Video Object Inpainting,
ICCV21(14448-14458)
IEEE DOI
2203
Training, Shape, Roads, Object oriented modeling, Coherence,
Benchmark testing, Image and video synthesis,
Vision applications and systems
BibRef
Cao, C.J.[Chen-Jie],
Fu, Y.W.[Yan-Wei],
Learning a Sketch Tensor Space for Image Inpainting of Man-made
Scenes,
ICCV21(14489-14498)
IEEE DOI
2203
Visualization, Tensors, Image edge detection,
Computational modeling, Image restoration, Decoding,
BibRef
Suin, M.[Maitreya],
Purohit, K.[Kuldeep],
Rajagopalan, A.N.,
Distillation-guided Image Inpainting,
ICCV21(2461-2470)
IEEE DOI
2203
Training, Deep learning, Neural networks, Buildings, Coherence,
Generators, Computational photography, Image and video synthesis,
Low-level and physics-based vision
BibRef
Suvorov, R.[Roman],
Logacheva, E.[Elizaveta],
Mashikhin, A.[Anton],
Remizova, A.[Anastasia],
Ashukha, A.[Arsenii],
Silvestrov, A.[Aleksei],
Kong, N.[Naejin],
Goka, H.[Harshith],
Park, K.[Kiwoong],
Lempitsky, V.[Victor],
Resolution-robust Large Mask Inpainting with Fourier Convolutions,
WACV22(3172-3182)
IEEE DOI
2202
Convolutional codes, Training, Costs,
Computational modeling, Network architecture, GANs Semantic Image Manipulation
BibRef
Zhang, J.,
Tang, S.,
Zhang, X.,
Li, Y.,
Zhang, R.,
Ahff-Net: Adaptive Hierarchical Feature Fusion Network For Image
Inpainting,
ICIP20(478-482)
IEEE DOI
2011
Image edge detection, Training, Generators,
Decoding, Adaptive systems, Image quality, feature fusion,
inpainting
BibRef
Li, J.,
He, F.,
Zhang, L.,
Du, B.,
Tao, D.,
Progressive Reconstruction of Visual Structure for Image Inpainting,
ICCV19(5961-5970)
IEEE DOI
2004
Code, Inpainting.
WWW Link. convolutional neural nets, image restoration,
neural net architecture, corrupted images, Shape
BibRef
Hedjazi, M.A.,
Genç, Y.,
Learning to Inpaint by Progressively Growing the Mask Regions,
Preregister19(4591-4596)
IEEE DOI
2004
image colour analysis, image restoration,
image retrieval, image segmentation, image texture,
generative adversarial networks
BibRef
Han, X.,
Wu, Z.,
Huang, W.,
Scott, M.,
Davis, L.,
FiNet: Compatible and Diverse Fashion Image Inpainting,
ICCV19(4480-4490)
IEEE DOI
2004
clothing, clothing industry, image representation,
image restoration, FiNet, fashion image inpainting,
Image reconstruction
BibRef
Nazeri, K.,
Ng, E.,
Joseph, T.,
Qureshi, F.,
Ebrahimi, M.,
EdgeConnect: Structure Guided Image Inpainting using Edge Prediction,
AIM19(3265-3274)
IEEE DOI
2004
edge detection, image resolution, image restoration,
learning (artificial intelligence),
convolutional neural network
BibRef
Su, Y.,
Liu, T.,
Liu, K.,
Liu, H.,
Pei, S.,
Image Inpainting For Random Areas Using Dense Context Features,
ICIP19(4679-4683)
IEEE DOI
1910
Deep learning, image inpainting, partial convolution, dense blocks, random area
BibRef
Renaudeau, A.[Arthur],
Lauze, F.[François],
Pierre, F.[Fabien],
Aujol, J.F.[Jean-François],
Durou, J.D.[Jean-Denis],
Alternate Structural-Textural Video Inpainting for Spot Defects
Correction in Movies,
SSVM19(104-116).
Springer DOI
1909
BibRef
Zhao, Y.,
Price, B.,
Cohen, S.,
Gurari, D.,
Guided Image Inpainting: Replacing an Image Region by Pulling Content
From Another Image,
WACV19(1514-1523)
IEEE DOI
1904
convolutional neural nets, image restoration, image region,
deep generative models, surrounding context, end-to-end network,
Training data
BibRef
Fayer, J.,
Morin, G.,
Gasparini, S.,
Daisy, M.,
Coudrin, B.,
Radiometric confidence criterion for patch-based inpainting,
ICPR18(2723-2728)
IEEE DOI
1812
Image resolution, Cameras, Data structures, Boolean functions,
Geometry, Distortion
BibRef
Zhang, X.,
Hamann, B.,
Pan, X.,
Zhang, C.,
Superpixel-based image inpainting with simple user guidance,
ICIP17(3785-3789)
IEEE DOI
1803
Computer science, Image edge detection, Image segmentation,
Measurement, Painting, Sun, image inpainting, user guidance
BibRef
Wang, W.,
Huang, Q.,
You, S.,
Yang, C.,
Neumann, U.,
Shape Inpainting Using 3D Generative Adversarial Network and
Recurrent Convolutional Networks,
ICCV17(2317-2325)
IEEE DOI
1802
decoding, feedforward neural nets, graphics processing units,
image colour analysis, image recognition, image reconstruction,
BibRef
Yeh, R.A.,
Chen, C.,
Lim, T.Y.,
Schwing, A.G.,
Hasegawa-Johnson, M.[Mark],
Do, M.N.,
Semantic Image Inpainting with Deep Generative Models,
CVPR17(6882-6890)
IEEE DOI
1711
Encoding, Image coding, Manifolds, Semantics, Training
BibRef
Zeng, Y.[Yu],
Lin, Z.[Zhe],
Yang, J.[Jimei],
Zhang, J.M.[Jian-Ming],
Shechtman, E.[Eli],
Lu, H.C.[Hu-Chuan],
High-resolution Image Inpainting with Iterative Confidence Feedback and
Guided Upsampling,
ECCV20(XIX:1-17).
Springer DOI
2011
BibRef
Yang, C.[Chao],
Lu, X.[Xin],
Lin, Z.[Zhe],
Shechtman, E.[Eli],
Wang, O.[Oliver],
Li, H.[Hao],
High-Resolution Image Inpainting Using Multi-scale Neural Patch
Synthesis,
CVPR17(4076-4084)
IEEE DOI
1711
Feature extraction, Image resolution, Mathematical model,
Neural networks, Optimization, Training
BibRef
Akl, A.,
Saad, E.,
Yaacoub, C.,
Structure-based image inpainting,
IPTA16(1-6)
IEEE DOI
1703
image restoration
BibRef
Koppel, M.[Marin],
Ben Makhlouf, M.[Mehdi],
Muller, K.[Karsen],
Wiegand, T.[Thomas],
Fast image completion method using patch offset statistics,
ICIP15(1795-1799)
IEEE DOI
1512
Inpainting
BibRef
Köhler, R.[Rolf],
Schuler, C.[Christian],
Schölkopf, B.[Bernhard],
Harmeling, S.[Stefan],
Mask-Specific Inpainting with Deep Neural Networks,
GCPR14(523-534).
Springer DOI
1411
BibRef
Daisy, M.[Maxime],
Buyssens, P.[Pierre],
Tschumperlé, D.[David],
Lézoray, O.[Olivier],
Tensor-Directed Spatial Patch Blending for Pattern-Based Inpainting
Methods,
CAIP15(I:149-160).
Springer DOI
1511
BibRef
Earlier: A1, A3, Only:
Spatial Patch Blending for Artefact Reduction in Pattern-Based
Inpainting Techniques,
CAIP13(II:523-530).
Springer DOI
1311
BibRef
Tae-o-sot, S.[Sarawut],
Nishihara, A.[Akinori],
Iterative Gradient-Driven Patch-Based Inpainting,
PSIVT11(II: 71-81).
Springer DOI
1111
BibRef
Zhou, H.L.[Hai-Ling],
Zheng, J.M.[Jian-Min],
Adaptive patch size determination for patch-based image completion,
ICIP10(421-424).
IEEE DOI
1009
BibRef
Vehkapera, J.[Janne],
Tomperi, S.[Seppo],
Replacing picture regions in H.264/AVC bitstream by utilizing
independent slices,
ICIP10(3397-3400).
IEEE DOI
1009
BibRef
Cheung, V.[Vincent],
Jojic, N.[Nebojsa],
Samaras, D.[Dimitris],
Capturing long-range correlations with patch models,
CVPR07(1-8).
IEEE DOI
0706
Apply to object registration, inpainting, relighting.
BibRef
de Rosa, A.,
Bonacchi, A.M.,
Cappellini, V.,
Barni, M.,
Image Segmentation and Region Filling for Virtual Restoration of
Artworks,
ICIP01(I: 562-565).
IEEE DOI
0108
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Inpainting, Video Inpainting .