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Image inpainting
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Springer DOI
1702
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1809
Image completion, Image inpainting,
Convolutional neural network, Failure detection
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Elsevier DOI
2006
Image inpainting, Irregular mask, Deep learning,
Attention mechanism, Unet-like network
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Image inpainting using scene constraints,
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2103
Image inpainting, Deep learning, Generative adversarial networks
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Yin, X.Y.[Xiao-Yan],
Hu, Z.Q.[Zhi-Qun],
Zheng, J.F.[Jia-Feng],
Li, B.Y.[Bo-Yong],
Zuo, Y.Y.[Yuan-Yuan],
Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning
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RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
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BibRef
Jam, J.[Jireh],
Kendrick, C.[Connah],
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A comprehensive review of past and present image inpainting methods,
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Elsevier DOI
2101
Survey, Inpainting. Image inpainting, Restoration, Texture synthesis,
Convolutional neural network, Generative adversarial networks
BibRef
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Bai, H.H.[Hui-Hui],
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Multi-scale attention network for image inpainting,
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Elsevier DOI
2102
Image inpainting, Multi-scale neural network,
Attention mechanism, Spatial attention, Channel attention
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Bai, H.H.[Hui-Hui],
Zhao, Y.[Yao],
Multi-level augmented inpainting network using spatial similarity,
PR(126), 2022, pp. 108547.
Elsevier DOI
2204
Image inpainting, Spatial information, Spatial similarity,
Pyramid reconstruction structure
BibRef
Lahiri, A.,
Bairagya, S.,
Bera, S.,
Haldar, S.,
Biswas, P.K.,
Lightweight Modules for Efficient Deep Learning Based Image
Restoration,
CirSysVideo(31), No. 4, April 2021, pp. 1395-1410.
IEEE DOI
2104
Convolution, Image restoration, Task analysis, Neural networks,
Kernel, Computational modeling, Image denoising, image inpainting,
efficient neural networks
BibRef
Zeng, Y.[Yuan],
Gong, Y.[Yi],
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Feature learning and patch matching for diverse image inpainting,
PR(119), 2021, pp. 108036.
Elsevier DOI
2106
Diverse image inpainting, Free-form mask, U-Net-like network, Nearest neighbors
BibRef
Meng, X.D.[Xiang-Dong],
Ma, W.[Wei],
Li, C.H.[Chun-Hu],
Mi, Q.[Qing],
Siamese CNN-based rank learning for quality assessment of inpainted
images,
JVCIR(78), 2021, pp. 103176.
Elsevier DOI
2107
Image inpainting, Rank learning, Image quality assessment, Siamese network
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Shao, H.[Hang],
Wang, Y.X.[Yong-Xiong],
Generative image inpainting with salient prior and relative total
variation,
JVCIR(79), 2021, pp. 103231.
Elsevier DOI
2109
Image inpainting, GAN, Corruption recognition, Salient prior,
Relative total variation
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Xu, L.M.[Li-Ming],
Zeng, X.H.[Xian-Hua],
Li, W.S.[Wei-Sheng],
Bai, L.[Ling],
IDHashGAN: Deep Hashing With Generative Adversarial Nets for
Incomplete Data Retrieval,
MultMed(24), 2022, pp. 534-545.
IEEE DOI
2202
Image restoration, Kernel, Manifolds, Image reconstruction, Training,
Deep learning, Generative adversarial nets, hash learning,
supervised manifold similarity
BibRef
Wu, H.W.[Hai-Wei],
Zhou, J.T.[Jian-Tao],
IID-Net: Image Inpainting Detection Network via Neural Architecture
Search and Attention,
CirSysVideo(32), No. 3, March 2022, pp. 1172-1185.
IEEE DOI
2203
Feature extraction, Forensics, Forgery, Training, Task analysis,
Semantics, Inpainting forensics,
deep neural networks
BibRef
Wu, H.W.[Hai-Wei],
Zhou, J.T.[Jian-Tao],
Li, Y.M.[Yuan-Man],
Deep Generative Model for Image Inpainting With Local Binary Pattern
Learning and Spatial Attention,
MultMed(24), 2022, pp. 4016-4027.
IEEE DOI
2208
Feature extraction, Generators, Decoding, Task analysis, Semantics,
Image edge detection, Correlation, Image inpainting, LBP,
deep learning
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Manickam, A.[Adhiyaman],
Jiang, J.M.[Jian-Min],
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DOI Link
2212
BibRef
Xiang, H.Y.[Han-Yu],
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Nawaz, M.A.[Muhammad Ali],
Huang, X.F.[Xian-Feng],
Zhang, F.[Fan],
Yu, H.K.[Hong-Kai],
Deep learning for image inpainting: A survey,
PR(134), 2023, pp. 109046.
Elsevier DOI
2212
Survey, Inpainting. Image inpainting, Image restoration,
Generative adversarial network, Convolutional neural network
BibRef
Huang, W.L.[Wen-Li],
Deng, Y.[Ye],
Hui, S.Q.[Si-Qi],
Wang, J.J.[Jin-Jun],
Image Inpainting with Bilateral Convolution,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Deng, Y.[Ye],
Hui, S.Q.[Si-Qi],
Meng, R.Y.[Rong-Ye],
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Wang, J.J.[Jin-Jun],
Hourglass Attention Network for Image Inpainting,
ECCV22(XVIII:483-501).
Springer DOI
2211
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Kim, J.[Jinwoo],
Kim, W.[Woojae],
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Lee, S.H.[Sang-Hoon],
Progressive Contextual Aggregation Empowered by Pixel-Wise Confidence
Scoring for Image Inpainting,
IP(32), 2023, pp. 1200-1214.
IEEE DOI
2302
Image resolution, Semantics, Generators, Image restoration, Training,
Task analysis, Image edge detection, Image inpainting, adversarial learning
BibRef
Chen, Y.T.[Yuan-Tao],
Xia, R.L.[Run-Long],
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FFTI: Image inpainting algorithm via features fusion and two-steps
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JVCIR(91), 2023, pp. 103776.
Elsevier DOI
2303
BibRef
And:
Corrigendum:
JVCIR(93), 2023, pp. 103802.
Elsevier DOI
2305
Image inpainting, Deep learning, Two-steps inpainting,
Attention mechanism, Features fusion
BibRef
Chen, Y.T.[Yuan-Tao],
Xia, R.L.[Run-Long],
Yang, K.[Kai],
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MFMAM: Image inpainting via multi-scale feature module with attention
module,
CVIU(238), 2024, pp. 103883.
Elsevier DOI
2312
Image inpainting, Deep learning, Multi-scale feature,
Deep level features, Attention module
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Li, H.Y.[Hai-Yan],
Song, Y.Q.[Ying-Qing],
Li, H.[Haijiang],
Wang, Z.Y.[Zheng-Yu],
Semantic prior-driven fused contextual transformation network for
image inpainting,
JVCIR(91), 2023, pp. 103777.
Elsevier DOI
2303
Image inpainting, Semantic prior generator,
Fused contextual transformation, Discriminator
BibRef
Ma, Y.Q.[Yu-Qing],
Liu, X.L.[Xiang-Long],
Bai, S.H.[Shi-Hao],
Wang, L.[Lei],
Liu, A.[Aishan],
Tao, D.C.[Da-Cheng],
Hancock, E.R.[Edwin R.],
Regionwise Generative Adversarial Image Inpainting for Large Missing
Areas,
Cyber(53), No. 8, August 2023, pp. 5226-5239.
IEEE DOI
2307
Generators, Semantics, Task analysis, Feature extraction, Correlation,
Computer architecture, Image restoration, regionwise convolutions
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Li, J.F.[Jian-Fei],
Huang, C.Y.[Chao-Yan],
Chan, R.[Raymond],
Feng, H.[Han],
Ng, M.K.[Michael K.],
Zeng, T.Y.[Tie-Yong],
Spherical Image Inpainting with Frame Transformation and Data-Driven
Prior Deep Networks,
SIIMS(16), No. 3, 2023, pp. 1177-1194.
DOI Link
2309
BibRef
Yu, X.X.[Xue-Xin],
Xu, L.[Long],
Li, J.[Jia],
Ji, X.Y.[Xiang-Yang],
MagConv: Mask-Guided Convolution for Image Inpainting,
IP(32), 2023, pp. 4716-4727.
IEEE DOI
2309
BibRef
Xiang, H.Y.[Hong-Yue],
Min, W.D.[Wei-Dong],
Wei, Z.[Zitai],
Zhu, M.[Meng],
Liu, M.X.[Meng-Xue],
Deng, Z.Y.[Zi-Yang],
Image inpainting network based on multi-level attention mechanism,
IET-IPR(18), No. 2, 2024, pp. 428-438.
DOI Link
2402
image processing, image restoration, Image inpainting,
vanilla convolution, gated convolution, multi-level attention mechanism
BibRef
Wang, Y.[Yechen],
Song, B.[Bin],
Zhang, Z.Y.[Zhi-Yong],
An image inpainting method based on generative adversarial networks
inversion and autoencoder,
IET-IPR(18), No. 4, 2024, pp. 1042-1052.
DOI Link
2403
image processing, neural nets
BibRef
Sheng, Z.Q.[Zi-Qi],
Xu, W.B.[Wen-Bo],
Lin, C.[Cong],
Lu, W.[Wei],
Ye, L.[Long],
Deep generative network for image inpainting with gradient semantics
and spatial-smooth attention,
JVCIR(98), 2024, pp. 104014.
Elsevier DOI
2402
Image content security, Image inpainting,
Deep generative model, Spatial-smooth attention
BibRef
Chen, B.W.[Bo-Wei],
Liu, T.J.[Tsung-Jung],
Liu, K.H.[Kuan-Hsien],
Image Inpainting by Mscswin Transformer Adversarial Autoencoder,
ICIP23(2040-2044)
IEEE DOI Code:
WWW Link.
2312
BibRef
Chen, P.[Peifu],
Zhang, J.W.[Jian-Wei],
Han, G.Q.[Guo-Qiang],
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Image Inpainting with Multi-scale Consistent Features,
ICPR22(266-273)
IEEE DOI
2212
Deep learning, Image resolution, Refining, Neural networks, Filling,
Faces
BibRef
Li, Z.X.[Zi-Xuan],
Wang, Y.G.[Yuan-Gen],
Optimizing Transformer for Large-Hole Image Inpainting,
ICIP23(1180-1184)
IEEE DOI Code:
WWW Link.
2312
BibRef
Lin, J.Y.[Jia-Yu],
Wang, Y.G.[Yuan-Gen],
Tang, W.Z.[Wen-Zhi],
Li, A.F.[Ai-Feng],
Multi-feature Co-learning for Image Inpainting,
ICPR22(296-302)
IEEE DOI
2212
Source coding, Benchmark testing
BibRef
Cao, C.J.[Chen-Jie],
Dong, Q.[Qiaole],
Fu, Y.W.[Yan-Wei],
Learning Prior Feature and Attention Enhanced Image Inpainting,
ECCV22(XV:306-322).
Springer DOI
2211
BibRef
Zheng, H.T.[Hai-Tian],
Lin, Z.[Zhe],
Lu, J.W.[Jing-Wan],
Cohen, S.[Scott],
Shechtman, E.[Eli],
Barnes, C.[Connelly],
Zhang, J.M.[Jian-Ming],
Xu, N.[Ning],
Amirghodsi, S.[Sohrab],
Luo, J.B.[Jie-Bo],
Image Inpainting with Cascaded Modulation GAN and Object-Aware Training,
ECCV22(XVI:277-296).
Springer DOI
2211
BibRef
Yu, Y.S.[Yong-Sheng],
Zhang, L.[Libo],
Fan, H.[Heng],
Luo, T.J.[Tie-Jian],
High-Fidelity Image Inpainting with GAN Inversion,
ECCV22(XVI:242-258).
Springer DOI
2211
BibRef
Ni, Y.Y.[Yuan-Yuan],
Cheng, W.G.[Wen-Gang],
Dual Path Cross-Scale Attention Network For Image Inpainting,
ICIP22(4223-4227)
IEEE DOI
2211
Image resolution, Image coding, Fuses, Decoding,
Image reconstruction, Image inpainting, contextual attention, decoder
BibRef
Li, W.B.[Wen-Bo],
Lin, Z.[Zhe],
Zhou, K.[Kun],
Qi, L.[Lu],
Wang, Y.[Yi],
Jia, J.Y.[Jia-Ya],
MAT: Mask-Aware Transformer for Large Hole Image Inpainting,
CVPR22(10748-10758)
IEEE DOI
2210
Image quality, Convolutional codes, Image resolution,
Computational modeling, Modulation,
Low-level vision
BibRef
Li, X.G.[Xiao-Guang],
Guo, Q.[Qing],
Lin, D.[Di],
Li, P.[Ping],
Feng, W.[Wei],
Wang, S.[Song],
MISF:Multi-level Interactive Siamese Filtering for High-Fidelity
Image Inpainting,
CVPR22(1859-1868)
IEEE DOI
2210
Measurement, Filtering, Computational modeling, Semantics,
Image filtering, Pattern recognition, Image restoration,
Deep learning architectures and techniques
BibRef
Wang, W.T.[Wen-Tao],
Niu, L.[Li],
Zhang, J.[Jianfu],
Yang, X.[Xue],
Zhang, L.Q.[Li-Qing],
Dual-path Image Inpainting with Auxiliary GAN Inversion,
CVPR22(11411-11420)
IEEE DOI
2210
Codes, Semantics, Generative adversarial networks, Generators,
Pattern recognition, Feeds, Image and video synthesis and generation
BibRef
Dong, Q.[Qiaole],
Cao, C.J.[Chen-Jie],
Fu, Y.W.[Yan-Wei],
Incremental Transformer Structure Enhanced Image Inpainting with
Masking Positional Encoding,
CVPR22(11348-11358)
IEEE DOI
2210
Image resolution, Image coding, Computational modeling, Gray-scale,
Transformers, Encoding, Image and video synthesis and generation
BibRef
Cipolina-Kun, L.[Lucia],
Caenazzo, S.[Simone],
Mazzei, G.[Gaston],
Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting Completing
M.C Escher's Print Gallery,
NTIRE22(715-723)
IEEE DOI
2210
Degradation, Image resolution, Computational modeling,
Digital art, Image restoration, Pattern recognition
BibRef
Cai, J.Y.[Jia-Yin],
Li, C.L.[Chang-Lin],
Tao, X.[Xin],
Tai, Y.W.[Yu-Wing],
Image Multi-Inpainting via Progressive Generative Adversarial
Networks,
NTIRE22(977-986)
IEEE DOI
2210
Training, Learning systems, Adaptation models, Shape, Semantics,
Performance gain, Generative adversarial networks
BibRef
Wang, W.T.[Wen-Tao],
Zhang, J.F.[Jian-Fu],
Niu, L.[Li],
Ling, H.Y.[Hao-Yu],
Yang, X.[Xue],
Zhang, L.Q.[Li-Qing],
Parallel Multi-Resolution Fusion Network for Image Inpainting,
ICCV21(14539-14548)
IEEE DOI
2203
Degradation, Fuses, Convolution, Coherence,
Streaming media, Image and video synthesis,
BibRef
likowski, M.P.[Marcin Przewiez],
Smieja, M.[Marek],
Struski, L.[Lukasz],
Tabor, J.[Jacek],
MisConv: Convolutional Neural Networks for Missing Data,
WACV22(2917-2926)
IEEE DOI
2202
Adaptation models, Uncertainty, Convolution, Image processing,
Neural networks, Estimation, Machine learning, Deep Learning Image Processing
BibRef
Liu, H.Y.[Hong-Yu],
Wan, Z.Y.[Zi-Yu],
Huang, W.[Wei],
Song, Y.B.[Yi-Bing],
Han, X.T.[Xin-Tong],
Liao, J.[Jing],
PD-GAN: Probabilistic Diverse GAN for Image Inpainting,
CVPR21(9367-9376)
IEEE DOI
2111
Image synthesis, Handheld computers, Modulation, Process control,
Probabilistic logic, Filling, Image restoration
BibRef
Hukkelås, H.[Håkon],
Lindseth, F.[Frank],
Mester, R.[Rudolf],
Image Inpainting with Learnable Feature Imputation,
GCPR20(388-403).
Springer DOI
2110
BibRef
Wang, T.F.[Teng-Fei],
Ouyang, H.[Hao],
Chen, Q.F.[Qi-Feng],
Image Inpainting with External-internal Learning and Monochromic
Bottleneck,
CVPR21(5116-5125)
IEEE DOI
2111
Deep learning, Image color analysis,
Superresolution, Semantics, Filling, Pattern recognition
BibRef
Jam, J.[Jireh],
Kendrick, C.[Connah],
Drouard, V.[Vincent],
Walker, K.[Kevin],
Hsu, G.S.[Gee-Sern],
Yap, M.H.[Moi Hoon],
R-MNet: A Perceptual Adversarial Network for Image Inpainting,
WACV21(2713-2722)
IEEE DOI
2106
Training, Image resolution, Shape, Computational modeling,
Training data, Visual systems
BibRef
Chen, C.[Cong],
Abbott, A.[Amos],
Stilwell, D.[Daniel],
Multi-Level Generative Chaotic Recurrent Network for Image Inpainting,
WACV21(3625-3634)
IEEE DOI
2106
Training, Degradation, Recurrent neural networks,
Adaptive systems, Benchmark testing
BibRef
Yenamandra, S.[Sriram],
Khurana, A.[Ansh],
Jena, R.[Rohit],
Awate, S.P.[Suyash P.],
Learning Image Inpainting from Incomplete Images using
Self-Supervision,
ICPR21(10390-10397)
IEEE DOI
2105
Training, Semantics, Neural networks, Estimation,
Optimization, Faces
BibRef
Ma, X.[Xin],
Zhou, X.Q.[Xiao-Qiang],
Huang, H.B.[Huai-Bo],
Chai, Z.H.[Zhen-Hua],
Wei, X.L.[Xiao-Lin],
He, R.[Ran],
Free-Form Image Inpainting via Contrastive Attention Network,
ICPR21(9242-9249)
IEEE DOI
2105
Deep learning, Image resolution, Shape, Semantics, Robustness,
Decoding
BibRef
Lahiri, A.,
Jain, A.K.,
Agrawal, S.,
Mitra, P.,
Biswas, P.K.,
Prior Guided GAN Based Semantic Inpainting,
CVPR20(13693-13702)
IEEE DOI
2008
Image reconstruction, Training, Semantics,
Image resolution, Computational modeling, Generative adversarial networks
BibRef
Siavelis, P.R.[Panagiotis-Rikarnto],
Lamprinou, N.[Nefeli],
Psarakis, E.Z.[Emmanouil Z.],
An Improved GAN Semantic Image Inpainting,
ACIVS20(443-454).
Springer DOI
2003
BibRef
Saad, A.B.,
Tamaazousti, Y.,
Kherroubi, J.,
He, A.,
Where Is The Fake? Patch-Wise Supervised GANS For Texture Inpainting,
ICIP20(568-572)
IEEE DOI
2011
Image segmentation, Task analysis, Generators, Training,
Generative adversarial networks, Convolution,
Segmentation
BibRef
Zhao, L.,
Mo, Q.,
Lin, S.,
Wang, Z.,
Zuo, Z.,
Chen, H.,
Xing, W.,
Lu, D.,
UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space
Translation,
CVPR20(5740-5749)
IEEE DOI
2008
Training, Manifolds, Image restoration, Semantics, Image generation
BibRef
Lahiri, A.,
Jain, A.K.,
Nadendla, D.,
Biswas, P.K.,
Faster Unsupervised Semantic Inpainting: A GAN Based Approach,
ICIP19(2706-2710)
IEEE DOI
1910
Generative Adversarial Networks, Semantic Inpainting,
Temporal Consistency, Video Inpainting
BibRef
Zhang, P.,
Liu, W.,
Lei, Y.,
Lu, H.,
Yang, X.,
Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene
Completion,
ICCV19(7800-7809)
IEEE DOI
2004
convolutional neural nets, feature extraction, image resolution,
image restoration, learning (artificial intelligence), Image segmentation
BibRef
Xie, C.,
Liu, S.,
Li, C.,
Cheng, M.,
Zuo, W.,
Liu, X.,
Wen, S.,
Ding, E.,
Image Inpainting With Learnable Bidirectional Attention Maps,
ICCV19(8857-8866)
IEEE DOI
2004
Code, Inpainting.
WWW Link. convolutional neural nets, feature extraction,
image colour analysis, image restoration, Image reconstruction
BibRef
Gupta, P.,
Rahtu, E.,
CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific
Image Inpainting and Image Denoising,
ICCV19(6707-6716)
IEEE DOI
2004
backpropagation, image denoising, image reconstruction,
image restoration, neural nets, security of data, Neural networks
BibRef
Yu, J.,
Lin, Z.,
Yang, J.,
Shen, X.,
Lu, X.,
Huang, T.,
Free-Form Image Inpainting With Gated Convolution,
ICCV19(4470-4479)
IEEE DOI
2004
Code, Inpainting.
WWW Link. computational geometry, convolutional neural nets,
feature extraction, feature selection, image restoration, Training
BibRef
Kahatapitiya, K.[Kumara],
Tissera, D.[Dumindu],
Rodrigo, R.[Ranga],
Context-Aware Automatic Occlusion Removal,
ICIP19(1895-1899)
IEEE DOI
1910
Deep Learning, Context-Awareness, Occlusion Removal
BibRef
Altinel, F.,
Ozay, M.,
Okatani, T.,
Deep Structured Energy-Based Image Inpainting,
ICPR18(423-428)
IEEE DOI
1812
Training, Generative adversarial networks,
Minimization, Convolutional neural networks, Benchmark testing, Task analysis
BibRef
Hsu, C.,
Chen, F.,
Wang, G.,
High-Resolution Image Inpainting through Multiple Deep Networks,
ICVISP17(76-81)
IEEE DOI
1712
Signal processing, Deep Learning, Image Inpainting, Super Resolution
BibRef
Fawzi, A.,
Samulowitz, H.,
Turaga, D.,
Frossard, P.[Pascal],
Image inpainting through neural networks hallucinations,
IVMSP16(1-5)
IEEE DOI
1608
Biological neural networks
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Missing Data, Fixing Problems .