Alilou, V.K.[Vahid K.],
Yaghmaee, F.[Farzin],
Application of GRNN neural network in non-texture image inpainting
and restoration,
PRL(62), No. 1, 2015, pp. 24-31.
Elsevier DOI
1507
Image inpainting
BibRef
Cai, N.[Nian],
Su, Z.H.[Zheng-Hang],
Lin, Z.N.[Zhi-Neng],
Wang, H.[Han],
Yang, Z.J.[Zhi-Jing],
Ling, B.W.K.[Bingo Wing-Kuen],
Blind inpainting using the fully convolutional neural network,
VC(33), No. 2, February 2017, pp. 249-261.
Springer DOI
1702
BibRef
Tanaka, T.[Takahiro],
Kawai, N.[Norihiko],
Nakashima, Y.[Yuta],
Sato, T.[Tomokazu],
Yokoya, N.[Naokazu],
Iterative applications of image completion with CNN-based failure
detection,
JVCIR(55), 2018, pp. 56-66.
Elsevier DOI
1809
Image completion, Image inpainting,
Convolutional neural network, Failure detection
BibRef
Wang, N.[Ning],
Ma, S.[Sihan],
Li, J.Y.[Jing-Yuan],
Zhang, Y.P.[Yi-Peng],
Zhang, L.F.[Le-Fei],
Multistage attention network for image inpainting,
PR(106), 2020, pp. 107448.
Elsevier DOI
2006
Image inpainting, Irregular mask, Deep learning,
Attention mechanism, Unet-like network
BibRef
Hedjazi, M.A.[Mohamed Abbas],
Genc, Y.[Yakup],
Image inpainting using scene constraints,
SP:IC(93), 2021, pp. 116148.
Elsevier DOI
2103
Image inpainting, Deep learning, Generative adversarial networks
BibRef
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
Algorithm,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Jam, J.[Jireh],
Kendrick, C.[Connah],
Walker, K.[Kevin],
Drouard, V.[Vincent],
Hsu, J.G.S.[Jison Gee-Sern],
Yap, M.H.[Moi Hoon],
A comprehensive review of past and present image inpainting methods,
CVIU(203), 2021, pp. 103147.
Elsevier DOI
2101
Survey, Inpainting. Image inpainting, Restoration, Texture synthesis,
Convolutional neural network, Generative adversarial networks
BibRef
Qin, J.[Jia],
Bai, H.[Huihui],
Zhao, Y.[Yao],
Multi-scale attention network for image inpainting,
CVIU(204), 2021, pp. 103155.
Elsevier DOI
2102
Image inpainting, Multi-scale neural network,
Attention mechanism, Spatial attention, Channel attention
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],
Zhang, J.[Jin],
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
BibRef
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
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.[Huaibo],
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 .