Nagy, James G.; Palmer, Katrina; Perrone, Lisa Iterative methods for image deblurring: A Matlab object-oriented approach. (English) Zbl 1048.65039 Numer. Algorithms 36, No. 1, 73-93 (2004). Summary: In iterative image restoration methods, implementation of efficient matrix vector multiplication, and linear system solves for preconditioners, can be a tedious and time consuming process. Different blurring functions and boundary conditions often require implementing different data structures and algorithms. A complex set of computational methods is needed, each likely having different input parameters and calling sequences. This paper describes a set of Matlab tools that hide these complicated implementation details. Combining the powerful scientific computing and graphics capabilities in Matlab, with the ability to do object-oriented programming and operator overloading, results in a set of classes that is easy to use, and easily extensible. Cited in 68 Documents MSC: 65F22 Ill-posedness and regularization problems in numerical linear algebra 65F35 Numerical computation of matrix norms, conditioning, scaling 94A08 Image processing (compression, reconstruction, etc.) in information and communication theory 68W30 Symbolic computation and algebraic computation Keywords:image restoration; ill-posed problem; regularization; Matlab; object-oriented programming; iterative methods; preconditioning; numerical examples Software:RestoreTools; Matlab; Regularization tools PDFBibTeX XMLCite \textit{J. G. Nagy} et al., Numer. Algorithms 36, No. 1, 73--93 (2004; Zbl 1048.65039) Full Text: DOI