Abstract
In this paper we propose a new two-dimensional least mean squares algorithm (2D-LMS) which is able to track nonstationarities in both vertical and horizontal directions with a computational load comparable to 1D-LMS methods of the same number of weights. The main difference of our method consists in the proposed strategy to run the image in order to update the filter weights. Smaller initial transients, as well as a reduction in computational load and storage are achieved. Simulations comparing the behavior of our method to recently published methods of 2D-LMS adaptive filtering, have been carried out, showing the main advantages of the proposed method.
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