Two-dimensional adaptive filters based on the one-dimensional recursive least squares algorithm

✍ Scribed by Mitsuji Muneyasu; Eiji Uemoto; Takao Hinamoto

• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 645 KB
• Volume: 79
• Category: Article
• ISSN: 1042-0967
• DOI: 10.1002/ecjc.4430790709

✦ Synopsis

This paper proposes a method for realizing twodimensional (2-D) adaptive filters by applying a onedimensional (1-D) recursive least squares (RLS) algorithm. First, by applying a 1-D RLS algorithm along horimntal and vertical directions, a novel 2-D adaptive algorithm is developed. It is shown that the amount of calculations in the proposed algorithm is less than that in the existing 2-D RLS algorithm. Moreover, the convergence properties of the proposed algorithm and its relation to the conventional 2-D RLS algorithm are investigated. A method for accelerating the rate of convergence of the algorithm using Q priori estimation error also is described.

The proposed filter has good performance in nonstationary processes and the accuracy of convergence is better than that in the existing 2-D adaptive filters based on the least mean square (LMS) algorithm. Therefore, the proposed filter is suitable for processing an image with strongly nonstationary characteristics. Finally, the utility of the proposed filter is illustrated by applying it to 2-D system identification under a nonstationary environment as well as noise reduction of an image.