11.14.3.9.7 Inpainting, Patch Based Methods, Region Methods

Chapter Contents (Back)
Inpainting. Patch, Inpainting.
See also Outpainting, Extrapolation.

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


Zhang, L.Z.[Ling-Zhi], Barnes, C.[Connelly], Wampler, K.[Kevin], Amirghodsi, S.[Sohrab], Shechtman, E.[Eli], Lin, Z.[Zhe], Shi, J.B.[Jian-Bo],
Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-curation,
ECCV22(XVII:51-67).
Springer DOI 2211
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, Pattern recognition, 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, Pattern recognition 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

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Inpainting, Video Inpainting .


Last update:Mar 16, 2024 at 20:36:19