A Design Approach for 2-D Linear-Phase Filters with Quadrant Symmetry

A neural networks approach (NNA) was presented to design two-dimensional (2D) linear-phase finite-impulse response (FIR) digital filters with quadrantally symmetric magnitude response. To illustrate the feasibility of the NNA, a back-propagation neural-networks (BPNN) model was chosen and the stability of the BPNN was proved. The solution is presented as a parallel algorithm to approximate the desired magnitude response specification. Thus, the method avoids matrix inversion, and makes a fast calculation of the filter´s coefficients possible. The implementation of the approach was described together with some design guidelines, and optimal design examples were also given to demonstrate the effectiveness of the proposed approach