Grodzevich, Oleg2006-08-222006-08-2220042004http://hdl.handle.net/10012/1159We present a new method for regularization of ill-conditioned problems that extends the traditional trust-region approach. Ill-conditioned problems arise, for example, in image restoration or mathematical processing of medical data, and involve matrices that are very ill-conditioned. The method makes use of the L-curve and L-curve maximum curvature criterion as a strategy recently proposed to find a good regularization parameter. We describe the method and show its application to an image restoration problem. We also provide a MATLAB code for the algorithm. Finally, a comparison to the CGLS approach is given and analyzed, and future research directions are proposed.application/pdf653697 bytesapplication/pdfenCopyright: 2004, Grodzevich, Oleg. All rights reserved.Mathematicsregularizationill-posedinverse imaging problemnumerically hardrobustnessalgorithmsprogrammingefficiencyconjugate gradientMaster Thesis