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.

• Type: Journal
• Year: 2015
• Where: SCALESPACE
• Authors: Jérôme Fehrenbach, Mila Nikolova, Gabriele Steidl, Pierre Weiss
• Keywords: Computer Vision, SCALESPACE 2015