Thresholding techniques that account for noise are essential for the efficiency and accuracy of an optical communication or optical data storage system. Various types of noise in the system can result in error. To recover the data from the noisy signal, the error must be corrected by a fast and accurate signal processing algorithm. By considering the crosstalk effect of the neighboring channels, we have devised a multi-level thresholding method to set the threshold values based on the neighboring channel values. We compare the binary characterization performance of a neural network and the local multi-level adaptive thresholding method for decoding noisy transmission images. We show that the multi-thresholding implementation results in an average of 57.42% less binary characterization errors than the artificial neural network across twenty unique mixed noise optical conditions.

Date Published: 20 April 2015
PDF: 13 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770O (20 April 2015); doi: 10.1117/12.2180402

Published in SPIE Proceedings Vol. 9477:
Optical Pattern Recognition XXVI
David Casasent; Mohammad S. Alam, Editor(s)