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10 October 2020 High resolution video inpainting based on spatial structure and temporal edge information
Dezhi Bo, Ran Ma, Keke Wang, Kai Li, Ping An
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Proceedings Volume 11550, Optoelectronic Imaging and Multimedia Technology VII; 1155018 (2020) https://doi.org/10.1117/12.2574393
Event: SPIE/COS Photonics Asia, 2020, Online Only
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Abstract
Video inpainting is a very challenging task. Directly using the image inpainting method to repair the damaged video leads to the inter-frame contents flicker due to temporal discontinuities. In this paper, we introduce spatial structure and temporal edge information guided video inpainting model to repair the missing regions in high-resolution video. The model uses a convolutional neural network with residual blocks to fix up the missing contents in intra-frame according to spatial structure. At the same time, temporal edge of reference frame is introduced in the temporal domain, which has a large guiding effect on improving the texture and reducing the inter-frame flicker. We train the model with regular and irregular masks on the YouTube high resolution video datasets, and the trained model is qualitatively and quantitatively evaluated on the test set, and the results show our method is superior to the previous methods.
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Dezhi Bo, Ran Ma, Keke Wang, Kai Li, and Ping An "High resolution video inpainting based on spatial structure and temporal edge information", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 1155018 (10 October 2020); https://doi.org/10.1117/12.2574393
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KEYWORDS
Video
Spatial resolution
Temporal resolution
Video processing
Convolutional neural networks
Data modeling
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