TY - JOUR
T1 - Convex Variational Formulation with Smooth Coupling for Multicomponent Signal Decomposition and Recovery
AU - Luis M. Briceño-Arias & Patrick L. Combettes
JO - Numerical Mathematics: Theory, Methods and Applications
VL - 4
SP - 485
EP - 508
PY - 2009
DA - 2009/02
SN - 2
DO - http://doi.org/10.4208/nmtma.2009.m9009s
UR - https://global-sci.org/intro/article_detail/nmtma/6037.html
KW - Convex optimization, denoising, image restoration, proximal algorithm, signal decomposition, signal recovery.
AB - <p style="text-align: justify;">A convex variational formulation is proposed to solve multicomponent signal
processing problems in Hilbert spaces. The cost function consists of a separable term,
in which each component is modeled through its own potential, and of a coupling term,
in which constraints on linear transformations of the components are penalized with
smooth functionals. An algorithm with guaranteed weak convergence to a solution to
the problem is provided. Various multicomponent signal decomposition and recovery
applications are discussed.</p>