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- W2739357881 abstract "Image inpainting is a classical inverse ill-posed problem. In this paper, we introduce a multi-filters guided low-rank tensor coding as a priori information to tackle it. The key innovation is to formulate multiple feature-domain tensors by convoluting the target image with multi-filters. Furthermore, by exploring a low-rank tensor coding, it can reduce the redundancy between sparse feature vectors at neighboring locations and improve the efficiency of the overall representation. The resulting non-convex model is iteratively tackled by gradient descent and low-rank pursuit procedure. The experimental results demonstrate that the proposed algorithm can faithfully recover image and outperform the current state-of-the-art approach." @default.
- W2739357881 created "2017-07-31" @default.
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- W2739357881 date "2017-06-01" @default.
- W2739357881 modified "2023-09-27" @default.
- W2739357881 title "Multi-filters guided low-rank tensor coding for image inpainting" @default.
- W2739357881 doi "https://doi.org/10.1109/icivc.2017.7984590" @default.
- W2739357881 hasPublicationYear "2017" @default.
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