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- W2983978307 abstract "This paper proposed a novel low-rank minimization method for depth map denoising. Firstly, the color image is used to help searching for similar patches of each patch in a noisy depth map. Then we assemble them into a data matrix. Because the similar patches usually lie in a very low rank subspace, the low rank minimization model is exploited to solve the depth map denoising problem. However the original convex constraints, such as nuclear norm constraint, may be suboptimal to the low rank. To improve the recovery performance, the weighted nuclear norm constraint is used in the low-rank minimization as a nonconvex regularization. By using iterative regularization technique and a generalized soft thresholding operator, low rank minimization with weighted nuclear norm regularization can be efficiently solved. The denoised data matrix can be obtained after several iterations. A patch may be covered by multiple data matrices. We average them as final depth values. Our method is compared with the other competing methods by experiments to validate its effectiveness." @default.
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- W2983978307 date "2019-09-01" @default.
- W2983978307 modified "2023-09-22" @default.
- W2983978307 title "A Low-Rank Minimization Method for Depth Map Denoising" @default.
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- W2983978307 doi "https://doi.org/10.1109/cchi.2019.8901925" @default.
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