PRECONDITIONED GL-CGLS METHOD USING REGULARIZATION PARAMETERS CHOSEN FROM THE GLOBAL GENERALIZED CROSS VALIDATION

• 발행기관: 충청수학회
• 저자명: SeYoung Oh SunJoo Kwon
• 간행물 정보: 『Journal of the Chungcheong Mathematical Society』Volume 27, No. 4, 675~688쪽, 전체 14쪽
• 주제분류: 자연과학 > 자연과학일반
• 파일형태: PDF
• 발행일자: 2014.11.30

영문 초록

In this paper, we present an e±cient way to deter- mine a suitable value of the regularization parameter using the global generalized cross validation and analyze the experimental results from preconditioned global conjugate gradient linear least squares(Gl-CGLS) method in solving image deblurring problems. Preconditioned Gl-CGLS solves general linear systems with multi-ple right-hand sides. It has been shown in [10] that this method can be e？？ectively applied to image deblurring problems. The regulariza- tion parameter, chosen from the global generalized cross validation, with preconditioned Gl-CGLS method can give better reconstruc-tions of the true image than other parameters considered in this study.

목차

1. Introduction

2. Review of GCV

3. Extension to the global GCV

4. Numerical experiments