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Automatic parameter selection for feature-enhanced radar image restoration

Conference Paper


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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.

Publication Date


  • 2010

Citation


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

Scopus Eid


  • 2-s2.0-77954950804

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1821&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/810

Start Page


  • 001123

End Page


  • 001127

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.

Publication Date


  • 2010

Citation


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

Scopus Eid


  • 2-s2.0-77954950804

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1821&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/810

Start Page


  • 001123

End Page


  • 001127