RT journal article
T1 Using the ℓ1-norm for Image-based tomographic reconstruction
A1 Calvino Gámez, José Juan
A1 Fernández Areizaga, Elena
A1 López Haro, Miguel
A1 Muñoz Ocaña, Juan Manuel
A1 Rodríguez Chía, Antonio Manuel
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
A2 Estadística e Investigación Operativa
K1 Electron tomography
K1 Image reconstruction
K1 Lagrangian relaxation
K1 Linear programming
AB This paper introduces an ℓ1-norm model based on Total Variation Minimization for tomographic reconstruction. The reconstructions produced by the proposed model are more accurate than those obtained with classical reconstruction models based on the ℓ2-norm. This model can be linearized and solved by linear programming techniques. Furthermore, the complementary slackness conditions can be exploited to reduce the dimension of the resulting formulation by removing unnecessary variables and constraints. Since the efficacy of the reduced formulation strongly depends on the quality of the dual-multipliers used when applying the reduction method, Lagrangian relaxation is used to obtain near-optimal multipliers. This allows solving larger instances in an efficient way.
PB Elsevier
SN 0957-4174
YR 2023
FD 2023-06-17
LK http://hdl.handle.net/10498/29164
UL http://hdl.handle.net/10498/29164
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026