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- W2326174983 abstract "The restoration of images corrupted by speckle noise is a key issue in medical images. In this paper, a novel sparse model is presented for speckle reduction in ultrasound (US) images. This model contains three terms: a patch-based sparse representation prior over dictionary learning, a pixel-based total variation (TV) regularization term, and a data-fidelity term capturing the statistics of Rayleigh noise. The split Bregman algorithm for the proposed model is presented to solve the optimization problem. Experimental results on real US images validate that the proposed method is able to keep accurately edges and preserve meaningful structural details of the images." @default.
- W2326174983 created "2016-06-24" @default.
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- W2326174983 date "2015-01-01" @default.
- W2326174983 modified "2023-09-22" @default.
- W2326174983 title "Ultrasound Image Denoising via Dictionary Learning and Total Variation Regularization" @default.
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- W2326174983 doi "https://doi.org/10.2991/icmmcce-15.2015.84" @default.
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