Journal
SIAM JOURNAL ON IMAGING SCIENCES
Volume 7, Issue 2, Pages 613-640Publisher
SIAM PUBLICATIONS
DOI: 10.1137/130929424
Keywords
stationary noise; Berry-Esseen theorem; Morozov principle; negative norm models; destriping; convex analysis and optimization
Categories
Funding
- ANR SPH-IM-3D [ANR-12-BSV5-0008]
- CNRS
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Additive or multiplicative stationary noise recently became an important issue in applied fields such as microscopy or satellite imaging. Relatively few works address the design of dedicated denoising methods compared to the usual white noise setting. We recently proposed a variational algorithm to tackle this issue. In this paper, we analyze this problem from a statistical point of view and provide deterministic properties of the solutions of the associated variational problems. In the first part of this work, we demonstrate that in many practical problems, the noise can be assimilated to a colored Gaussian noise. We provide a quantitative measure of the distance between a stationary process and the corresponding Gaussian process. In the second part, we focus on the Gaussian setting and analyze denoising methods which consist in minimizing the sum of a total variation term and an l(2) data fidelity term. While the constrained formulation of this problem allows us to easily tune the parameters, the Lagrangian formulation can be solved more efficiently since the problem is strongly convex. Our second contribution consists in providing analytical values of the regularization parameter in order to approximately reach a given noise level.
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