Efficient minimization method for a generalized total variation functional

Paul Rodríguez, Brendt Wohlberg

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

212 Citas (Scopus)

Resumen

Replacing the ℓ2 data fidelity term of the standard Total Variation (TV) functional with an ℓ1 data fidelity term has been found to offer a number of theoretical and practical benefits. Efficient algorithms for minimizing this ℓ1-TV functional have only recently begun to be developed, the fastest of which exploit graph representations, and are restricted to the denoising problem. We describe an alternative approach that minimizes a generalized TV functional, including both ℓ2-TV and ℓM1 -TV as special cases, and is capable of solving more general inverse problems than denoising (e.g., deconvolution). This algorithm is competitive with the graph-based methods in the denoising case, and is the fastest algorithm of which we are aware for general inverse problems involving a nontrivial forward linear operator.

Idioma originalInglés
Páginas (desde-hasta)322-332
Número de páginas11
PublicaciónIEEE Transactions on Image Processing
Volumen18
N.º2
DOI
EstadoPublicada - 2009

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