11.14.3.9.1 Inpainting, GAN, CNN, Neural Nets, Learning

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.H.[Hui-Hui], 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

Qin, J.[Jia], 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], 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

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 BibRef

Manickam, A.[Adhiyaman], Jiang, J.[Jianmin], Zhou, Y.[Yu],
Deep image inpainting via contextual modelling in ADCT domain,
IET-IPR(16), No. 14, 2022, pp. 3748-3757.
DOI Link 2212
BibRef

Xiang, H.Y.[Han-Yu], Zou, Q.[Qin], 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.[Rongye], Zhou, S.P.[San-Ping], Wang, J.J.[Jin-Jun],
Hourglass Attention Network for Image Inpainting,
ECCV22(XVIII:483-501).
Springer DOI 2211
BibRef

Kim, J.[Jinwoo], Kim, W.[Woojae], Oh, H.[Heeseok], 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], Zou, K.[Ke], Yang, K.[Kai],
FFTI: Image inpainting algorithm via features fusion and two-steps inpainting,
JVCIR(91), 2023, pp. 103776.
Elsevier DOI 2303
Image inpainting, Deep learning, Two-steps inpainting, Attention mechanism, Features fusion BibRef

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

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.[Wenbo], 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

Liu, Q.[Qiankun], Tan, Z.[Zhentao], Chen, D.D.[Dong-Dong], Chu, Q.[Qi], Dai, X.[Xiyang], Chen, Y.P.[Yin-Peng], Liu, M.[Mengchen], Yuan, L.[Lu], Yu, N.H.[Neng-Hai],
Reduce Information Loss in Transformers for Pluralistic Image Inpainting,
CVPR22(11337-11347)
IEEE DOI 2210
Quantization (signal), Image resolution, Transformers, Pattern recognition, Decoding, Computational efficiency, 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.[Ziyu], 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 .