9622119 Vogel The primary goal of this project is to develop robust, efficient computational methods for the solution of regularized inverse problems. Also to be addressed are regularization parameter selection schemes, and the development of improved regularization functionals. Applications will be carried out for important problems in image reconstruction and parameter identification. Inverse problems arise in a number of important applications. Perhaps the most well know of these is biomedical imaging, e.g., CAT scans and Magnetic Resonance Imaging (MRI). Other examples include satellite- based (remote) sensing of the earth and seismic data processing. These problems tend to be highly unstable, i.e., small errors in the data may give rise to enormous errors in the solution. They also tend to be very large--- involving thousands or even millions of unknowns. The goal of this project is to develop computational techniques to solve such problems which are both stable and efficient. The attainment of this goal will greatly enhance a number of important technologies.
