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- W2783166743 abstract "Recently, depth maps introduce a very effective representation for solving many fundamental computer vision problems. However, modern 3D scanning devices, such as TOF (Time Of Flight) cameras and Microsoft Kinect sensor, provide a huge unbalance between the resolution of the intensity image and its corresponding depth map. Here, we address the problem of single depth map up-sampling using a new non-local total variation decomposition process with a self-learning structured sparsity model. This technique considers the fact of the decomposed components should be regularized by different constraints, hence better representation for depth maps in sparse domain can be achieved. Using different datasets, experimental results demonstrate superior effectiveness in terms of qualitative and quantitative measures." @default.
- W2783166743 created "2018-01-26" @default.
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- W2783166743 date "2017-12-01" @default.
- W2783166743 modified "2023-10-14" @default.
- W2783166743 title "Single depth map super-resolution: A self-structured sparsity representation with non-local total variation technique" @default.
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- W2783166743 doi "https://doi.org/10.1109/intelcis.2017.8260025" @default.
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