Minimum Residual Method based Optimal Selection of Regularization Parameter in Image Restoration

Swamy, Yamuna Narayana and Yalavarthy, Phaneendra K (2016) Minimum Residual Method based Optimal Selection of Regularization Parameter in Image Restoration. In: International Conference on Signal and Information Processing (IConSIP), OCT 06-08, 2016, Nanded, INDIA.

PDF Int_Con_Sig_Inf_Pro_2016.pdf - Published Version Restricted to Registered users only Download (5MB) | Request a copy

Abstract

The linear inverse problem encountered in restoration of blurred noisy images is typically solved via Tikhonov minimization. The outcome (restored image) of such minimization is highly dependent on the choice of regularization parameter. In the absence of prior information about the noise levels in the blurred image, finding this regularization parameter in an automated way becomes extremely challenging. The available methods like Generalized Cross Validation (GCV) may not yield optimal results in all cases. A novel method that relies on minimal residual method for finding the regularization parameter automatically is proposed here and was systematically compared with the GCV method. It was shown that the proposed method performance is superior to the GCV method in providing high quality restored images in cases where the noise levels are high.

Item Type:	Conference Proceedings
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	12 Aug 2017 06:58
Last Modified:	12 Aug 2017 06:58
URI:	http://eprints.iisc.ac.in/id/eprint/57634

Actions (login required)

View Item