Fusion of Regularization Terms For Image Restoration

Max Mignotte
DIRO, Département d'Informatique et de Recherche Opérationnelle, CP 6128,
Succ. Centre-Ville, P.O. 6128, Montréal (Québec), H3C 3J7.


ABSTRACT

In this work, we propose an efficient regularized restoration model associating a spatial and a frequential regularizers in order to better modeled the intrinsic properties of the original image to be recovered and to obtain a better restoration result. An adaptive and rescaling scheme is also proposed to balance the influence of these two different regularization constraints and allowing to prevent that a overwhelming importance for one of them prevail over the other in order to efficiently fuse them during the iterative deconvolution process.


Restoration Results






Figure: Respectively Exp1, Exp2, Exp3, Exp4 ,Exp5, Exp6 (cf. paper)