Abstract
Video inpainting is the process of improving the information content in a video by removing irrelevant video objects and restoring lost or deteriorated parts utilizing the spatiotemporal features that are available from adjacent frames. This paper proposes an effective video inpainting technique utilizing the multi-dimensional data decomposition technique. In Tensor Robust Principal Component Analysis (TRPCA), a multi-dimensional data corrupted by gross errors is decomposed into a low multi-rank component and a sparse component. The proposed method employs an improved version of TRPCA called Re-weighted low-rank Tensor Decomposition (RWTD) to separate the true information and the irrelevant sparse components in a video. Through this, manual identification of the components which have to be removed is avoided. Subsequent inpainting algorithm fills the region with appropriate and visually plausible data. The capabilities of the proposed method are validated by applying into videos having moving sparse outliers in it. The experimental results reveal that the proposed method performs well compared with other techniques.
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Ravindran, A., Baburaj, M., George, S.N. (2018). Video Inpainting Based on Re-weighted Tensor Decomposition. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_21
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DOI: https://doi.org/10.1007/978-981-10-7895-8_21
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