Automatic parameter selection for feature-enhanced radar image restoration

Authors

Moeness G Amin, Villanova UniversityFollow
Cher Hau Seng, University of WollongongFollow
Son Lam Phung, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow

RIS ID

33609

Publication Details

Seng, C., Bouzerdoum, A., Phung, S. & Amin, M. (2010). Automatic parameter selection for feature-enhanced radar image restoration. 2010 IEEE Radar Conference (pp. 001123-001127). USA: IEEE.

Abstract

In this paper, we propose a new technique for optimum parameter selection in non-quadratic radar image restoration. Although both the regularization hyper-parameter and the norm value are influential factors in the characteristics of the formed restoration, most existing optimization methods either require memory intensive computation or prior knowledge of the noise. Here, we present a contrast measure-based method for automated hyper-parameter selection. The proposed method is then extended to optimize the norm value used in non-quadratic image formation and restoration. The proposed method is evaluated on the MSTAR public target database and compared to the GCV method. Experimental results show that the proposed method yields better image quality at a much reduced computational cost.