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- W4372262736 abstract "Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross-scene generalization, and generated masked areas often contain blurry artifacts. Predictive filtering is a method for restoring images, which predicts the most effective kernels based on the input scene. Motivated by this approach, we address image completion as a filtering problem. Deep feature-level semantic filtering is introduced to fill in missing information, while preserving local structure and generating visually realistic content. In particular, a Dual-path Cooperative Filtering (DCF) model is proposed, where one path predicts dynamic kernels, and the other path extracts multi-level features by using Fast Fourier Convolution to yield semantically coherent reconstructions. Experiments on three challenging image completion datasets show that our proposed DCF outperforms state-of-art methods." @default.
- W4372262736 created "2023-05-07" @default.
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- W4372262736 date "2023-06-04" @default.
- W4372262736 modified "2023-10-14" @default.
- W4372262736 title "Image Completion Via Dual-Path Cooperative Filtering" @default.
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- W4372262736 doi "https://doi.org/10.1109/icassp49357.2023.10097260" @default.
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