21 research outputs found
Generating structured non-smooth priors and associated primal-dual methods
The purpose of the present chapter is to bind together and extend some recent developments regarding data-driven non-smooth regularization techniques in image processing through the means of a bilevel minimization scheme. The scheme, considered in function space, takes advantage of a dualization framework and it is designed to produce spatially varying regularization parameters adapted to the data for well-known regularizers, e.g. Total Variation and Total Generalized variation, leading to automated (monolithic), image reconstruction workflows. An inclusion of the theory of bilevel optimization and the theoretical background of the dualization framework, as well as a brief review of the aforementioned regularizers and their parameterization, makes this chapter a self-contained one. Aspects of the numerical implementation of the scheme are discussed and numerical examples are provided
Dualization and automatic distributed parameter selection of total generalized variation via bilevel optimization
Total Generalized Variation (TGV) regularization in image reconstruction relies on an infimal convolution type combination of generalized first- and second-order derivatives. This helps to avoid the staircasing effect of Total Variation (TV) regularization, while still preserving sharp contrasts in images. The associated regularization effect crucially hinges on two parameters whose proper adjustment represents a challenging task. In this work, a bilevel optimization framework with a suitable statistics-based upper level objective is proposed in order to automatically select these parameters. The framework allows for spatially varying parameters, thus enabling better recovery in high-detail image areas. A rigorous dualization framework is established, and for the numerical solution, two Newton type methods for the solution of the lower level problem, i.e. the image reconstruction problem, and two bilevel TGV algorithms are introduced, respectively. Denoising tests confirm that automatically selected distributed regularization parameters lead in general to improved reconstructions when compared to results for scalar parameters
Total Generalized Variation for Manifold-valued Data
In this paper we introduce the notion of second-order total generalized
variation (TGV) regularization for manifold-valued data in a discrete setting.
We provide an axiomatic approach to formalize reasonable generalizations of TGV
to the manifold setting and present two possible concrete instances that
fulfill the proposed axioms. We provide well-posedness results and present
algorithms for a numerical realization of these generalizations to the manifold
setup. Further, we provide experimental results for synthetic and real data to
further underpin the proposed generalization numerically and show its potential
for applications with manifold-valued data
Image Restoration: A General Wavelet Frame Based Model and Its Asymptotic Analysis
Image restoration is one of the most important areas in imaging science.
Mathematical tools have been widely used in image restoration, where wavelet
frame based approach is one of the successful examples. In this paper, we
introduce a generic wavelet frame based image restoration model, called the
"general model", which includes most of the existing wavelet frame based models
as special cases. Moreover, the general model also includes examples that are
new to the literature. Motivated by our earlier studies [1-3], We provide an
asymptotic analysis of the general model as image resolution goes to infinity,
which establishes a connection between the general model in discrete setting
and a new variatonal model in continuum setting. The variational model also
includes some of the existing variational models as special cases, such as the
total generalized variational model proposed by [4]. In the end, we introduce
an algorithm solving the general model and present one numerical simulation as
an example
Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization
Total Generalized Variation (TGV) regularization in image reconstruction
relies on an infimal convolution type combination of generalized first- and
second-order derivatives. This helps to avoid the staircasing effect of Total
Variation (TV) regularization, while still preserving sharp contrasts in
images. The associated regularization effect crucially hinges on two parameters
whose proper adjustment represents a challenging task. In this work, a bilevel
optimization framework with a suitable statistics-based upper level objective
is proposed in order to automatically select these parameters. The framework
allows for spatially varying parameters, thus enabling better recovery in
high-detail image areas. A rigorous dualization framework is established, and
for the numerical solution, two Newton type methods for the solution of the
lower level problem, i.e. the image reconstruction problem, and two bilevel TGV
algorithms are introduced, respectively. Denoising tests confirm that
automatically selected distributed regularization parameters lead in general to
improved reconstructions when compared to results for scalar parameters