Adaptive generalized stack filtering under the mean-absolute-error criterion
Lin Yin, Jaakko T. Astola, Yrjo A. Neuvo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
A new adaptive algorithm is developed in this paper for determining optimal generalized stack (GS) filters under the mean absolute error criterion. This algorithm, based on the neural network representation of Boolean functions, is much more efficient than the traditional truth table based algorithms. This is because: (1) the number of variables to represent a GS filter is considerably reduced when a set of neurons is used to represent a GS filter, where the number of the variables is proportional to the filter window width, and (2) the procedure of enforcing the stacking constraints of GS filters is greatly simplified since a sufficient condition is derived under which the neurons satisfy the stacking property. Experimental results from image restoration are provided to demonstrate the performance of the new adaptive GS filters.