The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Resolution-robust Large Mask Inpainting with Fourier Convolutions
Open accessAbstract
Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function. To alleviate this issue, we propose a new method called large mask inpainting (LaMa). LaMa is based on i) a new inpainting network architecture that uses fast Fourier convolutions (FFCs), which have the image-wide receptive field; ii) a high receptive field perceptual loss; iii) large training masks, which unlocks the potential of the first two components. Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g. completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter&time costs than the competitive baselines. The code is available at https://github.com/saic-mdal/lama.
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High-Resolution Image Synthesis with Latent Diffusion Models
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RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01117 · Cited by 1,778
DiffIR: Efficient Diffusion Model for Image Restoration
Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01204 · Cited by 412
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., & Furon, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02053 · Cited by 251
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
Metadata
- Authors
- Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., & Lempitsky, V.
- Venue
- 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2022-01
- Pages
- 3172-3182
- OpenAlex
- W3199003182
- Cited by
- 1,123
- References
- 69
- FWCI
- 85.57
- Type
- conference paper
- Language
- en
- Open access
- green
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- Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., & Lempitsky, V. (2022). Resolution-robust Large Mask Inpainting with Fourier Convolutions. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv51458.2022.00323
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