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.