AN ADAPTIVE VARIATIONAL MODEL FOR MEDICAL IMAGES RESTORATION
Image denoising is one of the important tasks required by medical imaging analysis. In this work, we investigate an adaptive variation model for medical images restoration. In the proposed model, we have used the first-order total variation combined with Laplacian regularizer to eliminate the stairc...
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Published in | International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-2/W12; pp. 219 - 224 |
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Main Authors | , , , |
Format | Journal Article Conference Proceeding |
Language | English |
Published |
Gottingen
Copernicus GmbH
01.01.2019
Copernicus Publications |
Subjects | |
Online Access | Get full text |
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Summary: | Image denoising is one of the important tasks required by medical imaging analysis. In this work, we investigate an adaptive variation model for medical images restoration. In the proposed model, we have used the first-order total variation combined with Laplacian regularizer to eliminate the staircase effect in the first-order TV model while preserve edges of object in the piecewise constant image. We also propose an instance of Split Bregman method to solve the proposed denoising model as an optimization problem. Experimental results from mixed Poisson-Gaussian noise are given to demonstrate that our proposed approach outperforms the related methods. |
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ISSN: | 2194-9034 1682-1750 2194-9034 |
DOI: | 10.5194/isprs-archives-XLII-2-W12-219-2019 |