Abstract
This chapter focuses on singular value decomposition (SVD)-based algorithms for ill-posed problems in image restoration. It discusses the characteristics of solutions produced by various regularization methods, including truncated least squares, regularized least squares, regularized total least squares, and truncated total least squares. Economical ways are presented to compute each of these solutions using iterative methods. The chapter concludes with numerical experiments illustrating the effects of different regularizations.
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