The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
Open accessAbstract
Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.
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Cites
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Context Encoders: Feature Learning by Inpainting
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Cited by
Image Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419
“[34] takes the result from Content Encoders as input and then propagates the texture information from non-hole regions to fill the hole regions as postprocessing.”
BackgroundMethodGlobally and locally consistent image completion
Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073659 · Cited by 2,203
Resolution-robust Large Mask Inpainting with Fourier Convolutions
Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., & Lempitsky, V. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00323 · Cited by 1,123
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499
Generative Adversarial Networks: A Survey Toward Private and Secure Applications
Cai, Z., Xiong, Z., Xu, H., Wang, P., Li, W., & Pan, Y. (2022) · ACM Computing Surveys · doi:10.1145/3459992 · Cited by 376
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Zhao, S., Cui, J., Sheng, Y., Dong, Y., Liang, X., Chang, E. I., & Xu, Y. (2021) · arXiv · doi:10.48550/arxiv.2103.10428 · Cited by 224
A detailed analysis of image and video forgery detection techniques
Tyagi, S., & Yadav, D. (2023) · The Visual Computer · doi:10.1007/s00371-021-02347-4 · Cited by 109
Towards efficient and scale-robust ultra-high-definition image demoiréing
Yu, X., Dai, P., Li, W., Ma, L., Shen, J., Li, J., & Qi, X. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19797-0_37 · Cited by 93
FHDe2Net: Full High Definition Demoireing Network
He, B., Wang, C., Shi, B., & Duan, L.-Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_43 · Cited by 62
Deep Learning-Based X-ray Inpainting for Improving Spinal 2d-3d Registration
Esfandiari, H., Weidert, S., Kövesházi, I., Anglin, C., Street, J., & Hodgson, A. J. (2021) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2228 · Cited by 12
Metadata
- Authors
- Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., & Li, H.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4076-4084
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2557414982
- Cited by
- 877
- References
- 7
- FWCI
- 33.41
- Type
- conference paper
- Language
- en
- Open access
- green
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