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SCALESPACE
2015
Springer

Bilevel Image Denoising Using Gaussianity Tests

3 years 8 months ago
Bilevel Image Denoising Using Gaussianity Tests
Abstract. We propose a new methodology based on bilevel programming to remove additive white Gaussian noise from images. The lowerlevel problem consists of a parameterized variational model to denoise images. The parameters are optimized in order to minimize a specific cost function that measures the residual Gaussianity. This model is justified using a statistical analysis. We propose an original numerical method based on the Gauss-Newton algorithm to minimize the outer cost function. We finally perform a few experiments that show the well-foundedness of the approach. We observe a significant improvement compared to standard TV- 2 algorithms and show that the method automatically adapts to the signal regularity.
Jérôme Fehrenbach, Mila Nikolova, Gab
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SCALESPACE
Authors Jérôme Fehrenbach, Mila Nikolova, Gabriele Steidl, Pierre Weiss
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