6533b82efe1ef96bd12928a5

RESEARCH PRODUCT

Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence

Charles-alban DeledalleNicolas PapadakisJoseph SalmonSamuel Vaiter

subject

FOS: Computer and information sciences[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingStatistics - Machine Learning[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]RegularizationPseudo-inverse[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingMachine Learning (stat.ML)[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]Total-variation[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]Divergence

description

International audience; 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.

https://hal.archives-ouvertes.fr/hal-01412059