Image-Inpainting 图像修复 (补全) 论文汇总

论文速递 sophie ⋅ 于 2周前 ⋅ 586 阅读

修复指的是恢复图像损失的部分并且基于背景信息将它们重建的技术。它指的是在视觉输入的指定区域中填充缺失数据的过程。在数字世界中,它指的是应用复杂算法以替代图像数据中缺失或者损坏部分。本文汇总了Image-Inpainting图像修复(补全)的相关论文及代码资源,欢迎收藏阅读。

Awesome-Image-Inpainting

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作者:1900zyh
来源项目:Awesome-Image-Inpainting


Early methods (Non Learning Based)

[1] Bertalmio, M., Sapiro, G., Caselles, V., & Ballester, C. (2000, July). Image inpainting. In SIGGRAPH (pp. 417-424). [paper]

[2] Bertalmio, M., Vese, L., Sapiro, G., & Osher, S. (2003). Simultaneous structure and texture image inpainting. TIP, 12(8), 882-889. [paper]

[3] Criminisi, A., Pérez, P., & Toyama, K. (2004). Region filling and object removal by exemplar-based image inpainting. TIP 13(9), 1200-1212. [paper]

[4] Sun, J., Yuan, L., Jia, J., & Shum, H. Y. (2005, July). Image completion with structure propagation. ToG (Vol. 24, No. 3, pp. 861-868). [paper]

[5] Huang, J. B., Kang, S. B., Ahuja, N., & Kopf, J. (2014). Image completion using planar structure guidance. TOG, 33(4), 129. [paper] [code] [project]


Deep Architectures (Learning Based)

NeurIPS 2015

[1] Ren, J. S., Xu, L., Yan, Q., & Sun, W. (2015). Shepard convolutional neural networks. NeurIPS pp. 901-909). [paper] [code]


CVPR 2016

[1] Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. CVPR (pp. 2536-2544). [paper] [code]


Siggraph 2017

[1] Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2017). Globally and locally consistent image completion. ToG, 36(4), 107. [paper] [code] [project]


CVPR 2017

[1] Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., & Li, H. (2017). High-resolution image inpainting using multi-scale neural patch synthesis. CVPR (pp. 6721-6729). [paper] [code]

[2]Li, Y., Liu, S., Yang, J., & Yang, M. H. (2017). Generative face completion. CVPR (pp. 3911-3919). [paper] [code]

[3] Yeh, R. A., Chen, C., Yian Lim, T., Schwing, A. G., Hasegawa-Johnson, M., & Do, M. N. (2017). Semantic image inpainting with deep generative models. CVPR (pp. 5485-5493). [paper] [code] [project]

CVPR 2018

[1] Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas S. Huang, Generative Image Inpainting with Contextual Attention, CVPR, 2018. [paper] [code] [project]

[2] Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2018). Generative image inpainting with contextual attention. CVPR (pp. 5505-5514). [paper]

[3] Dolhansky, B., & Canton Ferrer, C. (2018). Eye in-painting with exemplar generative adversarial networks. CVPR (pp. 7902-7911). [paper] [project] [code]

[4] Deng, J., Cheng, S., Xue, N., Zhou, Y., & Zafeiriou, S. (2018). Uv-gan: Adversarial facial uv map completion for pose-invariant face recognition. CVPR (pp. 7093-7102). [paper]

[5] Gilbert, A., Collomosse, J., Jin, H., & Price, B. (2018). Disentangling Structure and Aesthetics for Style-aware Image Completion. CVPR (pp. 1848-1856). [paper]


ECCV 2018

[1] Liu, G., Reda, F. A., Shih, K. J., Wang, T. C., Tao, A., & Catanzaro, B. (2018). Image inpainting for irregular holes using partial convolutions. ECCV (pp. 85-100). [paper] [project]

[2] Song, Y., Yang, C., Lin, Z., Liu, X., Huang, Q., Li, H., & Jay Kuo, C. C. (2018). Contextual-based image inpainting: Infer, match, and translate. ECCV (pp. 3-19). [paper]

[3] Yan, Z., Li, X., Li, M., Zuo, W., & Shan, S. (2018). Shift-net: Image inpainting via deep feature rearrangement. ECCV (pp. 1-17). [paper] [code]

NeurIPS 2018

[1] Wang, Y., Tao, X., Qi, X., Shen, X., & Jia, J. (2018). Image Inpainting via Generative Multi-column Convolutional Neural Networks. NeurIPS (pp. 331-340). [paper] [code]

BMVC 2018

[1] Song, Y., Yang, C., Shen, Y., Wang, P., Huang, Q., & Kuo, C. C. J. (2018). SPG-Net: Segmentation prediction and guidance network for image inpainting. BMVC. [paper]

MM 2018

[1] Vo, H. V., Duong, N. Q., & Pérez, P. (2018, October). Structural inpainting. MM (pp. 1948-1956). [paper]

[2] Zhang, H., Hu, Z., Luo, C., Zuo, W., & Wang, M. (2018, October). Semantic Image Inpainting with Progressive Generative Networks. MM (pp. 1939-1947). [paper] [code]

ACCV 2018

[1] Liao, H., Funka-Lea, G., Zheng, Y., Luo, J., & Zhou, S. K. (2018). Face Completion with Semantic Knowledge and Collaborative Adversarial Learning. ACCV. [paper]

ICASSP 2018

[1] Liao, L., Hu, R., Xiao, J., & Wang, Z. (2018, April). Edge-Aware Context Encoder for Image Inpainting. ICASSP (pp. 3156-3160). [paper]


ACM Transactions on Graphics (TOG) 2018

[1] Portenier, T., Hu, Q., Szabó, A., Bigdeli, S. A., Favaro, P., & Zwicker, M. (2018). Faceshop: Deep sketch-based face image editing. TOG, 37(4), 99. [paper]

Arxiv 2018

[1] Chen, Z., Nie, S., Wu, T., & Healey, C. G. (2018). High resolution face completion with multiple controllable attributes via fully end-to-end progressive generative adversarial networks. arXiv preprint arXiv:1801.07632. [paper]

[2] Banerjee, S., Scheirer, W. J., Bowyer, K. W., & Flynn, P. J. (2018). On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs. arXiv preprint arXiv:1811.07104. [paper]

[3] Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2018). Free-form image inpainting with gated convolution. arXiv preprint arXiv:1806.03589. [paper] [project]


CVPR 2019

[1] Zheng, C., Cham, T. J., & Cai, J. (2019). Pluralistic Image Completion. CVPR. [paper] [code] [project]

[2] Zeng, Y., Fu, J., Chao, H., & Guo, B. (2019). Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting. CVPR. [paper] [code]

[3] Xiong, W., Lin, Z., Yang, J., Lu, X., Barnes, C., & Luo, J. (2019). Foreground-aware Image Inpainting. CVPR. [paper]

[4] Han, X., Zhang, Z., Du, D., Yang, M., Yu, J., Pan, P., ... & Cui, S. (2019). Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image. CVPR. [paper]

[5] Sagong, M. C., Shin, Y. G., Kim, S. W., Park, S., & Ko, S. J. (2019). PEPSI: Fast Image Inpainting With Parallel Decoding Network. CVPR (pp. 11360-11368).

[6] Grigorev, A., Sevastopolsky, A., Vakhitov, A., & Lempitsky, V. (2019). Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation. CVPR (pp. 12135-12144). [paper]

[7] Xu, R., Li, X., Zhou, B., & Loy, C. C. (2019). Deep Flow-Guided Video Inpainting. CVPR. [paper][code]

[8] Kim, D., Woo, S., Lee, J. Y., & Kweon, I. S. (2019). Deep Video Inpainting. CVPR. [paper][code]


Arxiv 2019

[1] Nazeri, K., Ng, E., Joseph, T., Qureshi, F., & Ebrahimi, M. (2019). EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning. arXiv preprint arXiv:1901.00212. [paper] [code]

[2] Xiao, Q., Li, G., & Chen, Q. (2018). Deep Inception Generative Network for Cognitive Image Inpainting. arXiv preprint arXiv:1812.01458. [paper]

[3] Webster, R., Rabin, J., Simon, L., & Jurie, F. (2019). Detecting Overfitting of Deep Generative Networks via Latent Recovery. arXiv preprint arXiv:1901.03396. [paper]

[4] Jo, Y., & Park, J. (2019). SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. arXiv preprint arXiv:1902.06838. [paper] [code]

[5] Hong, X., Xiong, P., Ji, R., & Fan, H. (2019). Deep Fusion Network for Image Completion. arXiv preprint arXiv:1904.08060. [paper] [code]

[6] Shin, Y. G., Sagong, M. C., Yeo, Y. J., Kim, S. W., & Ko, S. J. (2019). PEPSI++: Fast and Lightweight Network for Image Inpainting. arXiv preprint arXiv:1905.09010. [paper]



项目地址:https://github.com/1900zyh/Awesome-Image-Inpainting

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