Title
A general framework for a class of non-linear approximations with applications to image restoration.
Abstract
In this paper, we establish sufficient conditions for the existence of optimal non-linear approximations to a linear subspace generated by a given weakly-closed (non-convex) cone of a Hilbert space. Most non-linear problems have difficulties to implement good projection-based algorithms due to the fact that the subsets, where we would like to project the functions, do not have the necessary geometric properties to use the classical existence results (such as convexity, for instance). The theoretical results given here overcome some of these difficulties. To see this we apply them to a fractional model for image deconvolution. In particular, we reformulate and prove the convergence of a computational algorithm proposed in a previous paper by some of the authors. Finally, some examples are given.
Year
DOI
Venue
2018
10.1016/j.cam.2017.03.008
Journal of Computational and Applied Mathematics
Keywords
Field
DocType
Non-linear approximation,Fractional deconvolution,Image restoration,Weakly-closed non-convex cone
Hilbert space,Convergence (routing),Mathematical optimization,Convexity,Nonlinear system,Mathematical analysis,Deconvolution,Non linear approximation,Linear subspace,Image restoration,Mathematics
Journal
Volume
Issue
ISSN
330
C
0377-0427
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Vicente F. Candela1154.59
Antonio Falcó2415.43
Pantaleón D. Romero371.88