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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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-2/W12; pp. 219 - 224
Main Authors Tran, T. T. T., Pham, C. T., Kopylov, A. V., Nguyen, V. N.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 01.01.2019
Copernicus Publications
Subjects
Algorithms
Archives & records
Biometrics
Information science
Medical imaging
Noise
Noise reduction
Optimization
Photogrammetry
Random noise
Remote sensing
Restoration
Surveillance
Variables
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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.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-2-W12-219-2019