This paper addresses the use of an adaptive noise canceling technique to eliminate the coherent noise generated in scanner data. The technique is based on a Finite Impulse Response (FIR) adaptive noise canceler. A two-weight FIR filter is used to adaptively learn the characteristics of a sinusoid. This sinusoid is then removed from the data. The least Mean Squares (LMS) algorithm is used to converge to the coefficients of the adaptive filter during the learning process. An image corrupted with a single frequency periodic noise is used for investigating the algorithm. It is observed that the efficiency of the algorithm is dependent on the convergence gains and the initial positioning of the weights of the FIR filter. Because of the computational simplicity of the algorithm, it is possible to implement this in real-time mode.

Date Published: 1 November 1991
PDF: 7 pages
Proc. SPIE 1541, Infrared Sensors: Detectors, Electronics, and Signal Processing, (1 November 1991); doi: 10.1117/12.49327

Published in SPIE Proceedings Vol. 1541:
Infrared Sensors: Detectors, Electronics, and Signal Processing
T. S. Jay Jayadev, Editor(s)