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We propose an iterative regularization algorithm in variable exponent Lebesgue spaces $L^p(.)$, which is able to automatically set up different regularization levels in different regions of the domain. Basically, modelling in $L^p(.)$ spaces allows to assign pointwise regularization parameters, associated to different values of the function parameter p(.). This is useful in image deblurring problems, where background, low intensity, and high intensity values of the image to restore require different filtering (i.e., regularization) levels.
This presentation is part of Minisymposium “MS71 - Nonlinear and adaptive regularization for image restoration”
organized by: Claudio Estatico (University of Genoa) , Giuseppe Rodriguez (University of Cagliari) .