MODEL-BASED IMAGE-RECONSTRUCTION BY MEANS OF A CONSTRAINED LEAST-SQUARES SOLUTION

The problem of the optimal use of object model information in image re construction is addressed. A closed-form solution for the estimated ob ject spectrum is derived with the Lagrange multiplier technique, which assumes a measured image, knowledge of the optical transfer function, statistical information about the measurement noise, and a model of t he object. This reconstruction algorithm is iterative in nature becaus e the optimal Lagrange multiplier is not generally known at the start of the problem. We derive the estimator, describe one technique for de termining the optimal Lagrange multiplier, demonstrate a stopping crit erion based on the mean-square error between a noise-free image and th e photon-limited version of the image, and show representative results for both filled- and sparse-aperture imaging applications. (C) 1997 O ptical Society of America.