Deblurring subject to nonnegativity constraints
Csiszar's I-divergence is used as a discrepancy measure for deblurring subject to the constraint that all functions involved are nonnegative. An iterative algorithm is proposed for minimizing this measure. It is shown that every function in the sequence is nonnegative and the sequence converges monotonically to a global minimum. Other properties of the algorithm are shown, including lower bounds on the improvement in the I-divergence at each step of the algorithm and on the difference between the I-difference at step k and at the limit point. A method for regularizing the solution is proposed. >