Image Inpainting: A Review
Tóm tắt
Từ khóa
Tài liệu tham khảo
Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2932058
Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2019.2908982
Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420–2432
Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779
Yu J, Yang X, Gao F, Tao D (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024
Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670
Hong C, Yu J, Tao D, Wang M (2014) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751
Yu J, Zhang B, Kuang Z, Lin D, Fan J (2016) iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12(5):1005–1016
Hong C, Yu J, Zhang J, Jin X, Lee K-H (2018) Multi-modal face pose estimation with multi-task manifold deep learning. IEEE Trans Ind Inform 15(7):3952–3961
Elharrouss O, Abbad A, Moujahid D, Riffi J, Tairi H (2016) A block-based background model for moving object detection. Electron Lett Comput Vis Image Anal 15(3):0017–31
Abbad A, Elharrouss O, Abbad K, Tairi H (2018) Application of meemd in post-processing of dimensionality reduction methods for face recognition. IET Biom 8(1):59–68
Moujahid D, Elharrouss O, Tairi H (2018) Visual object tracking via the local soft cosine similarity. Pattern Recognit Lett 110:79–85
Muddala SM, Olsson R, Sjöström M (2016) Spatio-temporal consistent depth-image-based rendering using layered depth image and inpainting. EURASIP J Image Video Process 2016(1):9
Isogawa M, Mikami D, Iwai D, Kimata H, Sato K (2018) Mask optimization for image inpainting. IEEE Access 6:69728–69741
Ružić T, Pižurica A (2014) Context-aware patch-based image inpainting using markov random field modeling. IEEE Trans Image Process 24(1):444–456
Jin KH, Ye JC (2015) Annihilating filter-based low-rank hankel matrix approach for image inpainting. IEEE Trans Image Process 24(11):3498–3511
Kawai N, Sato T, Yokoya N (2015) Diminished reality based on image inpainting considering background geometry. IEEE Trans Vis Comput Gr 22(3):1236–1247
Guo Q, Gao S, Zhang X, Yin Y, Zhang C (2017) Patch-based image inpainting via two-stage low rank approximation. IEEE Trans Vis Comput Gr 24(6):2023–2036
Lu H, Liu Q, Zhang M, Wang Y, Deng X (2018) Gradient-based low rank method and its application in image inpainting. Multimed Tools Appl 77(5):5969–5993
Xue H, Zhang S, Cai D (2017) Depth image inpainting: improving low rank matrix completion with low gradient regularization. IEEE Trans Image Process 26(9):4311–4320
Liu J, Yang S, Fang Y, Guo Z (2018) Structure-guided image inpainting using homography transformation. IEEE Trans Multimed 20(12):3252–3265
Ding D, Ram S, Rodríguez JJ (2018) Image inpainting using nonlocal texture matching and nonlinear filtering. IEEE Trans Image Process 28(4):1705–1719
Duan J, Pan Z, Zhang B, Liu W, Tai X-C (2015) Fast algorithm for color texture image inpainting using the non-local CTV model. J Glob Optim 62(4):853–876
Fan Q, Zhang L (2018) A novel patch matching algorithm for exemplar-based image inpainting. Multimed Tools Appl 77(9):10807–10821
Jiang W (2016) Rate-distortion optimized image compression based on image inpainting. Multimed Tools Appl 75(2):919–933
Alilou VK, Yaghmaee F (2017) Exemplar-based image inpainting using svd-based approximation matrix and multi-scale analysis. Multimed Tools Appl 76(5):7213–7234
Wang W, Jia Y (2017) Damaged region filling and evaluation by symmetrical exemplar-based image inpainting for thangka. EURASIP J Image Video Process 2017(1):38
Wei Y, Liu S (2016) Domain-based structure-aware image inpainting. Signal Image Video Process 10(5):911–919
Yao F (2018) Damaged region filling by improved criminisi image inpainting algorithm for thangka. Clust Comput 22:1–9
Zeng J, Fu X, Leng L, Wang C (2019) Image inpainting algorithm based on saliency map and gray entropy. Arabian J Sci Eng 44(4):3549–3558
Zhang D, Liang Z, Yang G, Li Q, Li L, Sun X (2018) A robust forgery detection algorithm for object removal by exemplar-based image inpainting. Multimed Tools Appl 77(10):11823–11842
Wali S, Zhang H, Chang H, Wu C (2019) A new adaptive boosting total generalized variation (TGV) technique for image denoising and inpainting. J Vis Commun Image Represent 59:39–51
Zhang Q, Lin J (2012) Exemplar-based image inpainting using color distribution analysis. J Inf Sci Eng 28(4):641–654
Liu Y, Caselles V (2012) Exemplar-based image inpainting using multiscale graph cuts. IEEE Trans Image process 22(5):1699–1711
Qin C, Chang C-C, Chiu Y-P (2013) A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Trans Image Process 23(3):969–978
Ghorai M, Samanta S, Mandal S, Chanda B (2019) Multiple pyramids based image inpainting using local patch statistics and steering kernel feature. IEEE Trans Image Process 28(11):5495–5509
Gao J, Zhu J, Nie K, Xu J An image inpainting method for interleaved 3D stacked image sensor, IEEE Sensors Journal
Borole RP, Bonde SV (2007) Image restoration using prioritized exemplar inpainting with automatic patch optimization. J Inst Eng India Ser B 98(3):311–319
Hoeltgen L (2017) Understanding image inpainting with the help of the Helmholtz equation. Math Sci 11(1):73–77
Zhang T, Gelman A, Laronga R (2017) Structure-and texture-based fullbore image reconstruction. Math Geosci 49(2):195–215
Li H, Luo W, Huang J (2017) Localization of diffusion-based inpainting in digital images. IEEE Trans Inf Forensics Secur 12(12):3050–3064
Li K, Wei Y, Yang Z, Wei W (2016) Image inpainting algorithm based on TV model and evolutionary algorithm. Soft Comput 20(3):885–893
Sridevi G, Kumar SS (2019) Image inpainting based on fractional-order nonlinear diffusion for image reconstruction. Circuit Syst Signal Process 38(8):1–16
Jin X, Su Y, Zou L, Wang Y, Jing P, Wang ZJ (2018) Sparsity-based image inpainting detection via canonical correlation analysis with low-rank constraints. IEEE Access 6:49967–49978
Mo J, Zhou Y (2018) The research of image inpainting algorithm using self-adaptive group structure and sparse representation. Cluster Comput 22(1):1–9
Yan Z, Li X, Li M, Zuo W, Shan S (2018) Shift-net: Image inpainting via deep feature rearrangement. In: Proceedings of the European conference on computer vision (ECCV), pp 1–17
Weerasekera CS, Dharmasiri T, Garg R, Drummond T, Reid I (2018) Just-in-time reconstruction: inpainting sparse maps using single view depth predictors as priors. In: 2018 IEEE international conference on robotics and automation (ICRA), IEEE, pp 1–9
Zhao J, Chen Z, Zhang L, Jin X (2018) Unsupervised learnable sinogram inpainting network (sin) for limited angle ct reconstruction. arXiv preprint arXiv:1811.03911
Chang Y-L, Yu Liu Z, Hsu W (2019) VORNet: spatio-temporally consistent video inpainting for object removal. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 0–0
Cai N, Su Z, Lin Z, Wang H, Yang Z, Ling BW-K (2017) Blind inpainting using the fully convolutional neural network. Vis Comput 33(2):249–261
Zhu X, Qian Y, Zhao X, Sun B, Sun Y (2018) A deep learning approach to patch-based image inpainting forensics. Signal Process Image Commun 67:90–99
Sidorov O, Hardeberg JY (2019) Deep hyperspectral prior: denoising, inpainting, super-resolution. arXiv preprint arXiv:1902.00301
Zeng Y, Fu J, Chao H, Guo B (2019) Learning pyramid-context encoder network for high-quality image inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1486–1494
Liu H, Jiang B, Xiao Y, Yang C (2019) Coherent semantic attention for image inpainting. arXiv preprint arXiv:1905.12384
Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA (2016) Context encoders: feature learning by inpainting, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2536–2544
Sasaki K, Iizuka S, Simo-Serra E, Ishikawa H (2017) Joint gap detection and inpainting of line drawings. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5725–5733
Hsu C, Chen F, Wang G (2017) High-resolution image inpainting through multiple deep networks, In: 2017 international conference on vision, image and signal processing (ICVISP), IEEE, pp 76–81
Nakamura T, Zhu A, Yanai K, Uchida S (2017) Scene text eraser. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), IEEE, Vol 1 pp 832–837
Xiang P, Wang L, Cheng J, Zhang B, Wu J (2017) A deep network architecture for image inpainting. In: 2017 3rd IEEE international conference on computer and communications (ICCC), IEEE, pp 1851–1856
Alilou VK, Yaghmaee F (2015) Application of GRNN neural network in non-texture image inpainting and restoration. Pattern Recognit Lett 62:24–31
Liao L, Hu R, Xiao J, Wang Z (2019) Artist-net: decorating the inferred content with unified style for image inpainting. IEEE Access 7:36921–36933
Cai X, Song B (2018) Semantic object removal with convolutional neural network feature-based inpainting approach. Multimed Syst 24(5):597–609
Hertz A, Fogel S, Hanocka R, Giryes R, Cohen-Or D (2019) Blind visual motif removal from a single image. arXiv preprint arXiv:1904.02756
Zhao Y, Price B, Cohen S, Gurari D (2019) Guided image inpainting: replacing an image region by pulling content from another image. In: 2019 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1514–1523
Su Y-Z, Liu T-J, Liu K-H, Liu H-H, Pei S-C (2019) Image inpainting for random areas using dense context features, In: 2019 IEEE international conference on image processing (ICIP), IEEE, pp 4679–4683
Ma Y, Liu X, Bai S, Wang L, He D, Liu A (2019) Coarse-to-fine image inpainting via region-wise convolutions and non-local correlation, In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI press, pp 3123–3129
Ke J, Deng J, Lu Y (2019) Noise reduction with image inpainting: an application in clinical data diagnosis, In: ACM SIGGRAPH 2019 posters, ACM, p 88
Liu S, Guo Z, Chen J, Yu T, Chen Z (2019) Interleaved zooming network for image inpainting, In: 2019 IEEE international conference on multimedia & expo workshops (ICMEW), IEEE, pp 673–678
Guo Z, Chen Z, Yu T, Chen J, Liu S (2019) Progressive image inpainting with full-resolution residual network, In: Proceedings of the 27th ACM international conference on multimedia, ACM, pp 2496–2504
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets, In: Advances in neural information processing systems, pp 2672–2680
Chen Y, Hu H (2019) An improved method for semantic image inpainting with gans: progressive inpainting. Neural Process Lett 49(3):1355–1367
Li J, Song G, Zhang M (2008) Occluded offline handwritten Chinese character recognition using deep convolutional generative adversarial network and improved GoogLeNet. Neural Comput Appl 1–15
Shin YG, Sagong MC, Yeo YJ, Kim SW, Ko SJ (2019) Pepsi++: fast and lightweight network for image inpainting. arXiv preprint arXiv:1905.09010
Wang H, Jiao L, Wu H, Bie R (2019) New inpainting algorithm based on simplified context encoders and multi-scale adversarial network. Procedia Comput Sci 147:254–263
Wang C, Xu C, Wang C, Tao D (2018) Perceptual adversarial networks for image-to-image transformation. IEEE Trans Image Process 27(8):4066–4079
Sagong M-c, Shin Y-g, Kim S-w, Park S, Ko S-j (2019) Pepsi: fast image inpainting with parallel decoding network, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11360–11368
Dhamo H, Tateno K, Laina I, Navab N, Tombari F (2019) Peeking behind objects: layered depth prediction from a single image. Pattern Recognit Lett 125:333–340
Elharrouss O, Al-Maadeed N, Al-Maadeed S (2019) Video summarization based on motion detection for surveillance systems. In: 15th international wireless communications & mobile computing conference (IWCMC). IEEE, pp 366–371
Almaadeed N, Elharrouss O, Al-Maadeed S, Bouridane A, Beghdadi A (2019) A novel approach for robust multi human action detection and recognition based on 3-dimentional convolutional neural networks. arXiv preprint arXiv:1907.11272
Vitoria P, Sintes J, Ballester C (2018) Semantic image inpainting through improved Wasserstein generative adversarial networks. arXiv preprint arXiv:1812.01071
Dong J, Yin R, Sun X, Li Q, Yang Y, Qin X (2018) Inpainting of remote sensing sst images with deep convolutional generative adversarial network. IEEE Geosci Remote Sens Lett 16(2):173–177
Lou S, Fan Q, Chen F, Wang C, Li J, (2018) Preliminary investigation on single remote sensing image inpainting through a modified gan, In: 2018 10th IAPR workshop on pattern recognition in remote sensing (PRRS). IEEE 1–6
Salem N.M, Mahdi HM, Abbas H (2018) Semantic image inpainting vsing self-learning encoder-decoder and adversarial loss, In: 2018 13th international conference on computer engineering and systems (ICCES), IEEE, pp 103–108
Liu H, Lu G, Bi X, Yan J, Wang W (2018) Image inpainting based on generative adversarial networks, In: 2018 14th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), IEEE, pp 373–378
Han X, Wu Z, Huang W, Scott MR, Davis LS (2019) Compatible and diverse fashion image inpainting. arXiv preprint arXiv:1902.01096
Jiao L, Wu H, Wang H, Bie R (2019) Multi-scale semantic image inpainting with residual learning and GAN. Neurocomputing 331:199–212
Nazeri K, Ng E, Joseph T, Qureshi F, Ebrahimi M (2019) Edgeconnect: generative image inpainting with adversarial edge learning. arXiv preprint arXiv:1901.00212
Li A, Qi J, Zhang R, Ma X, Ramamohanarao K (2019) Generative image inpainting with submanifold alignment. arXiv preprint arXiv:1908.00211
Li A, Qi J, Zhang R, Kotagiri R (2019) Boosted gan with semantically interpretable information for image inpainting, In: 2019 international joint conference on neural networks (IJCNN), IEEE, pp 1–8
Armanious K, Mecky Y, Gatidis S, Yang B (2019) Adversarial inpainting of medical image modalities, In: ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (ICASSP) IEEE, pp 3267–3271
Yeh R A, Chen C, Yian Lim T, Schwing AG, Hasegawa-Johnson M, Do MN (2017) Semantic image inpainting with deep generative models, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5485–5493
Doersch C, Singh S, Gupta A, Sivic J, Efros AA (2015) What makes paris look like paris? Commun ACM 58(12):103–110
Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A (2017) Places: a 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452–1464
Xiong W, Yu J, Lin Z, Yang J, Lu X, Barnes C, Luo J (2019) Foreground-aware image inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5840–5848
Martin D, Fowlkes C, Tal D, Malik J, et al (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Iccv Vancouver
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput vis 115(3):211–252
Liu Z, Luo P, Wang X, Tang X (2018) Large-scale celebfaces attributes (celeba) dataset, Retrieved August 15
Baumgardner M F, Biehl LL, Landgrebe DA (2015) 220 band aviris hyperspectral image data set: June 12, 1992 Indian pine test site 3. Purdue University Research Repository 10: R7RX991C
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, Cham, pp 740–755
Karatzas D, Shafait F, Uchida S, Iwamura M, i Bigorda LG, Mestre SR, Mas J, Mota DF, Almazan JA, De Las Heras LP (2013) ICDAR 2013 robust reading competition. In: 2013 12th international conference on document analysis and recognition, IEEE, pp 1484–1493
McCormac J, Handa A, Leutenegger S, Davison AJ (2016) Scenenet RGB-D: 5m photorealistic images of synthetic indoor trajectories with ground truth. arXiv preprint arXiv:1612.05079
Krause J, Stark M, Deng J, Fei-Fei L (2013) 3D object representations for fine-grained categorization, In: Proceedings of the IEEE international conference on computer vision workshops, pp 554–561
Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213–3223
Hirschmuller H, Scharstein D (2007) Evaluation of cost functions for stereo matching, In: 2007 IEEE conference on computer vision and pattern recognition, IEEE, pp 1–8
Scharstein D, Hirschmüller H, Kitajima Y, Krathwohl G, Nešić N, Wang X, Westling P (2014) High-resolution stereo datasets with subpixel-accurate ground truth. In: German conference on pattern recognition. Springer, cham, pp 31–42
Jboor NH, Belhi A, Al-Ali AK, Bouras A, Jaoua A (2019) Towards an inpainting framework for visual cultural heritage. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT), IEEE, pp 602–607