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- W2892165238 abstract "Exploiting sparsity in the image gradient magnitude has proved effective in preserving sharp edges and reducing noises for many image processing tasks. Based on observation, we build up a novel piecewise smooth segmentation model by utilizing a generalized total variation (TV) prior with $p$ -th power for $0 and a $ell^{1}$ data fidelity. We present an efficient algorithm based on the alternating direction method of multipliers (ADMM), where all subproblems can be solved by either one-step Gauss-Seidel iteration or the closed-form solution. Numerical experiments show that the proposed model can achieve more accurate segmentation results than the classical TV based segmentation model." @default.
- W2892165238 created "2018-09-27" @default.
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- W2892165238 date "2018-10-01" @default.
- W2892165238 modified "2023-09-23" @default.
- W2892165238 title "Piecewise Smooth Segmentation with Sparse Prior" @default.
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- W2892165238 doi "https://doi.org/10.1109/icip.2018.8451828" @default.
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