Training based optimal stack filter design under structural constraints

We develop a new procedure for the optimal stack filter design under structural constraints, e.g. for minimizing an error criterion and simultaneously preserving the shape of some signals. The training framework for optimal stack filter design developed by Tabus, Petrescu and Gabbouj (see IEEE Transactions on Image Processing, Special Issue on Nonlinear Image Processing, IP-5, p.1-18, June 1996) provides us with a proper setting for imposing structural constraints to the optimal filter, in order to preserve some desired details of the image. The target application is optimal stack filter design for image restoration, the goal being the "most efficient" noise attenuation.