Orthogonalization techniques for two-dimensional adaptive filters

Image and video signal processing applications often require filters with unknown or time-varying characteristics. Two-dimensional adaptive filters have been examined recently as a proposed solution to these problems. The following system considerations have driven research on cost-effective acceleration algorithms for 2-D adaptive filters. First the high data rates in digital video processing demand computational efficiency, and second, the nonstationary signal properties of images require optimized convergence speed. We present an overview of structures and algorithms developed to achieve an improved rate of convergence with reduced computational complexity. These include 2-D Newton-type adaptive filters and 2-D transform domain adaptive filters. The results are benchmarked against simple 2-D LMS and RLS adaptive filters.