Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
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en
Communication dans un congrès
Este ítem está publicado en
Signal Processing with Adaptive Sparse Structured Representations (SPARS'17), 2017-06-05, Lisbon. 2017
Resumen en inglés
We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solution remains constant. While ...Leer más >
We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solution remains constant. While this value is well know for the Lasso, such a critical value has not been investigated in details for the total-variation. Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought. We establish a closed form expression for the one-dimensional case, as well as an upper-bound for the two-dimensional case, that appears reasonably tight in practice. This problem is directly linked to the computation of the pseudo-inverse of the divergence, which can be quickly obtained by performing convolutions in the Fourier domain.< Leer menos
Palabras clave en inglés
Pseudo-inverse
Divergence
Regularization
Total-variation
Orígen
Importado de HalCentros de investigación