An Efficient Algorithm for 2-Dimensional Fir Filtering and System-Identification

In this paper a novel algorithm is presented for the efficient Two-Dimensional (2-D) Least Squares FIR filtering and system identification. Filter masks of general boundaries are allowed. Efficient order updating recursions are developed by exploiting the spatial shift invariance property of the 2-D data set. In contrast to the existing column(row)-wise 2-D recursive schemes based on the Levinson-Wiggins-Robinson's multichannel algorithm, the proposed technique offers the greatest maneuverability in the 2-D index space in a computational efficient way. This flexibility can be taken into advantage if the shape of the 2-D mask is not a priori known and has to be dynamically configured. The recursive character of the algorithm allows for a continuous reshaping of the filter mask. Search for the optimal filter mask, essentially reconfigures the filter mask to achieve an optimal match. The optimum determination of the mask shape offers important advantages in 2-D system modeling, filtering and image restoration.