Back Propagation Neural Network-Modified Covariance Modelling for Improvement of Holographic Imaging

Abstract

Two approaches are combined to improve the performance of modelling long wavelength holographic imaging (detection) of concealed objects. One approach is to design a multi-layer back propagation neural network that is able to reduce the effect of noise in a captured signal and results in a model as close as possible to the desired one. The other approach is to further process the captured signal by applying a modified covariance spectral estimation method to improve the resolution of the reconstructed image. Different concealing media and different values of signal to noise ratio are used to investigate the performance of such combination. It has been proved that the proposed model can provide an overall improvement in the imaging of concealed objects.

Key Words:

• Modified covariance method
• multi-layer neural network
• back propagation algorithm
• in-line holography

Additional information

Notes on contributors

L.B. Mohammed

Lubna Badri Mohammed received her B.Sc. and M.Sc. degrees, in Computer and Control Engineering, from the University of Technology, Baghdad, in 1994 and 1996, respectively, and her Ph.D. degree in Computer Engineering from the University of Technology in Baghdad, Iraq, in 1999. Her interest is in the fields of neural network and fuzzy logic, knowledge acquisition systems, and embedded system design. She has one book and more than 15 publications in reputed journals and conferences.

M.F. Al-Azzo

Mujahid Al-Azzo received his B.Sc. and M.Sc. degrees in Electrical Engineering and Electronic and Communication in 1982 and 1985, respectively, and his Ph.D. degree in Communication Engineering in 1999 all from Mosul University, Iraq. His interest is in the fields of signal processing, spectral analysis, and acoustical holographic imaging.