It is well known that, in general, nonconvex regularizers hold the potential for promoting sparsity more effectively than convex regularizers. To avoid the intrinsic difficulties related to nonconvex optimization, we present a new Convex Non-Convex variational model based on a more general parametric nonconvex regularizer which is applicable to a greater variety of image processing problems than prior CNC methods.. We derive the convexity conditions and related theoretical aspects of the CNC non-separable model.

This presentation is part of Minisymposium “MS37 - Sparse-based techniques in variational image processing (2 parts)”
organized by: Serena Morigi (Dept. Mathematics, University of Bologna) , Ivan Selesnick (New York University) , Alessandro Lanza (Dept. Mathematics, University of Bologna) .

Authors:

Ivan Selesnick (New York University)

Serena Morigi (Dept. Mathematics, University of Bologna)

Alessandro Lanza (Dept. Mathematics, University of Bologna)

Fiorella Sgallari (Dept. Mathematics, University of Bologna)

Keywords:

image deblurring, image reconstruction, inverse problems