Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/86953
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Efficient shift-variant image restoration using deformable filtering (Part I) |
Autor: | Miraut, David; Portilla, Javier CSIC ORCID CVN | Fecha de publicación: | 2012 | Editor: | Hindawi Publishing Corporation | Citación: | Eurasip Journal on Advances in Signal Processing 2012 (2012) | Resumen: | In this study, we propose using the least squares optimal deformable filtering approximation as an efficient tool for linear shift variant (SV) filtering, in the context of restoring SV-degraded images. Based on this technique we propose a new formalism for linear SV operators, from which an efficient way to implement the transposed SV-filtering is derived. We also provide a method for implementing an approximation of the regularized inversion of a SV-matrix, under the assumption of having smoothly spatially varying kernels, and enough regularization. Finally, we applied these techniques to implement a SV-version of a recent successful sparsity-based image deconvolution method. A high performance (high speed, high visual quality and low mean squared error, MSE) is demonstrated through several simulation experiments (one of them based on the Hubble telescope PSFs), by comparison to two state-of-the-art methods. © 2012 Springer | URI: | http://hdl.handle.net/10261/86953 | DOI: | 10.1186/1687-6180-2012-100 | Identificadores: | doi: 10.1186/1687-6180-2012-100 issn: 1687-6172 |
Aparece en las colecciones: | (CFMAC-IO) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
397915.pdf | 1,21 MB | Unknown | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
19
checked on 21-abr-2024
WEB OF SCIENCETM
Citations
13
checked on 23-feb-2024
Page view(s)
297
checked on 23-abr-2024
Download(s)
253
checked on 23-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.