2019 6th NAFOSTED Conference on Information and Computer Science (NICS) · 2019 · 12 citations · 18 references
ArtsBiomedical EngineeringBiomedical ImagingComputer VisionDigital ImagingDigital Image ProcessingImage AnalysisImage ProcessingImage QualityImage RestorationNoise ReductionDenoisingImage DenoisingImage Noise ReductionNoise RemovalRegularizationTotal Variation DenoisingTotal Variation Minimization
We propose an image denoising method by combining overlapping group sparsity and second-order total variation regularization. The method is named OGS-SOTV (image denoising method based on Overlapping Group Sparsity and Second-Order Total Variation regularization). The method utilizes performance of noise removal of overlapping group sparsity and performance of artifacts elimination of second-order total variation. A regularization parameter estimation is also proposed to implement the method automatically. In experiments, we compare denoising results of OGS-SOTV with ones of other similar methods. Results confirmed that OGS-SOTV works effectively and outperforms other similar denoising methods.
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The Split Bregman Method for L1-Regularized Problems
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Image restoration using total variation with overlapping group sparsity
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