A Non-Local Structure Tensor Based Approach for Multicomponent Image
Recovery Problems
release_axzuapcokrg2jajb6iub3gftb4
by
Giovanni Chierchia, Nelly Pustelnik, Beatrice Pesquet-Popescu,
Jean-Christophe Pesquet
2014
Abstract
Non-Local Total Variation (NLTV) has emerged as a useful tool in variational
methods for image recovery problems. In this paper, we extend the NLTV-based
regularization to multicomponent images by taking advantage of the Structure
Tensor (ST) resulting from the gradient of a multicomponent image. The proposed
approach allows us to penalize the non-local variations, jointly for the
different components, through various ℓ_1,p matrix norms with p > 1.
To facilitate the choice of the hyper-parameters, we adopt a constrained convex
optimization approach in which we minimize the data fidelity term subject to a
constraint involving the ST-NLTV regularization. The resulting convex
optimization problem is solved with a novel epigraphical projection method.
This formulation can be efficiently implemented thanks to the flexibility
offered by recent primal-dual proximal algorithms. Experiments are carried out
for multispectral and hyperspectral images. The results demonstrate the
interest of introducing a non-local structure tensor regularization and show
that the proposed approach leads to significant improvements in terms of
convergence speed over current state-of-the-art methods.
In text/plain
format
Archived Files and Locations
application/pdf 2.3 MB
file_n5xc4emiyrfulo3rexjj7zd2pu
|
arxiv.org (repository) web.archive.org (webarchive) |
1403.5403v2
access all versions, variants, and formats of this works (eg, pre-prints)