Abstract
We present a new 2-D optimum block stochastic gradient (TDOBSG) algorithm for 2-D adaptive finite impulse response (FIR) filtering. Unlike the 2-D optimum block adaptive (TDOBA) algorithm derived from a truncated Taylor's series expansion, which is in fact a suboptimum one, the TDOBSG algorithm exactly minimizes the squared norm of the a posteriori estimation error vector in a given block by optimally choosing the convergence factor of the adaptive filter. The optimum convergence factor can be computed from input signals at the same order of computational complexity as that of the TDOBA algorithm. Computer simulations based on the configuration of adaptive image noise cancellation show that the TDOBSG algorithm has better convergence speed and accuracy than those of the TDOBA algorithm.