Image Completion via Dual-path Cooperative Filtering

• URL: http://arxiv.org/abs/2305.00379v1
• Date: Sun, 30 Apr 2023 03:54:53 GMT
• Title: Image Completion via Dual-path Cooperative Filtering
• Authors: Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger
• Abstract summary: We propose a predictive filtering method for restoring images based on the input scene. Deep feature-level semantic filtering is introduced to fill in missing information. Experiments on three challenging image completion datasets show that our proposed DCF outperforms state-of-art methods.
• Score: 17.62197747945094
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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