Projection algorithms for two-dimensional adaptive filtering applications

In this paper we present a family of "new" two-dimensional adaptive filtering algorithms for image processing applications. These algorithms are multidimensional versions of the families of data-reusing and projection algorithms. These two classes of algorithms allow the adaptive filtering system designer to choose performance and computational complexity by changing parameters without actually changing algorithm structure. By changing parameters, the desired convergence rate can be achieved at the expense of additional computational complexity. Experiments show that significant improvement may be obtained by marginal increases in computational complexity over the traditional normalized LMS algorithm.