Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/89660
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Self-invertible 2D log-Gabor wavelets |
Autor: | Fischer, Sylvain CSIC; Sroubek, F.; Perrinet, Laurent; Redondo, Rafael CSIC; Cristóbal, Gabriel CSIC ORCID CVN | Palabras clave: | Image denoising Visual system Oriented high-pass filters Log-Gabor filters Wavelet transforms |
Fecha de publicación: | 2007 | Editor: | Springer Nature | Citación: | International Journal of Computer Vision 75: 231-246 (2007) | Resumen: | Orthogonal and biorthogonal wavelets became very popular image processing tools but exhibit major drawbacks, namely a poor resolution in orientation and the lack of translation invariance due to aliasing between subbands. Alternative multiresolution transforms which specifically solve these drawbacks have been proposed. These transforms are generally overcomplete and consequently offer large degrees of freedom in their design. At the same time their optimization gets a challenging task. We propose here the construction of log-Gabor wavelet transforms which allow exact reconstruction and strengthen the excellent mathematical properties of the Gabor filters. Two major improvements on the previous Gabor wavelet schemes are proposed: first the highest frequency bands are covered by narrowly localized oriented filters. Secondly, the set of filters cover uniformly the Fourier domain including the highest and lowest frequencies and thus exact reconstruction is achieved using the same filters in both the direct and the inverse transforms (which means that the transform is self-invertible). The present transform not only achieves important mathematical properties, it also follows as much as possible the knowledge on the receptive field properties of the simple cells of the Primary Visual Cortex (V1) and on the statistics of natural images. Compared to the state of the art, the log-Gabor wavelets show excellent ability to segregate the image information (e.g. the contrast edges) from spatially incoherent Gaussian noise by hard thresholding, and then to represent image features through a reduced set of large magnitude coefficients. Such characteristics make the transform a promising tool for processing natural images. © 2006 Springer Science+Business Media, LLC. | URI: | http://hdl.handle.net/10261/89660 | DOI: | 10.1007/s11263-006-0026-8 | Identificadores: | doi: 10.1007/s11263-006-0026-8 issn: 0920-5691 |
Aparece en las colecciones: | (CFMAC-IO) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
accesoRestringido.pdf | 15,38 kB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
133
checked on 12-abr-2024
WEB OF SCIENCETM
Citations
105
checked on 24-feb-2024
Page view(s)
297
checked on 22-abr-2024
Download(s)
104
checked on 22-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.