Denoising of Image Gradients and Total Generalized Variation Denoising

Abstract

We revisit total variation denoising and study an augmented model where we assume that an estimate of the image gradient is available. We show that this increases the image reconstruction quality and derive that the resulting model resembles the total generalized variation denoising method, thus providing a new motivation for this model. Further, we propose to use a constraint denoising model and develop a variational denoising model that is basically parameter free, i.e. all model parameters are estimated directly from the noisy image.Moreover, we use Chambolle-Pock's primal dual method as well as the Douglas-Rachford method for the new models. For the latter one has to solve large discretizations of partial differential equations. We propose to do this in an inexact manner using the preconditioned conjugate gradients method and derive preconditioners for this. Numerical experiments show that the resulting method has good denoising properties and also that preconditioning does increase convergence speed significantly. Finally we analyze the duality gap of different formulations of the TGV denoising problem and derive a simple stopping criterion.

Deflectometry for specular surfaces: an overview

“…Derivation of curvatures is less error-prone than shape integration and does not require accurate prior knowledge of the distance to the object. Differentiation amplifies noise (Komander, 2019;Komander et al, 2019) but-unlike integration-does not spread correlated errors over the surface.…”

Deflectometry for specular surfaces: an overview

“…Derivation of curvatures is less error-prone than shape integration and does not require accurate prior knowledge of the distance to the object. Differentiation amplifies noise [43,44] but (unlike integration) does not spread correlated errors over the surface.…”