Limited-angle reconstruction from noisy data using clustering of the solution space

The authors propose a stabilized method for limited angle reconstruction. The algorithm provides a framework that makes it possible to incorporate prior information and to account for the noise; this framework is the reason for the method's stability. The method entails the following steps: 1) a space of reconstruction solutions is generated, either analytically or by Monte Carlo simulation; 2) representatives of the solutions are found by clustering the solution space; and 3) these representatives are scored, using the a priori information, to define the final solution. Simulation results are presented for a simple 1-D deconvolution problem.