25 October 1996 Iterative model-based image reconstruction using a constrained least-squares technique
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Abstract
In this paper the problem of optimally using object model information in image reconstruction is addressed. A closed form solution for the estimated object spectrum is derived using 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 the object. This reconstruction algorithm is iterative in nature for two reasons: (1) because the optimal Lagrange multiplier is not generally known at the start of the problem; and (2) we can use the object estimate obtained from one step of the algorithm as the model input for the next step. In this paper we derive the estimator, describe one technique for determining the optimal Lagrange multiplier, demonstrate a stopping criterion based on the mean squared error between a noise free image and the photon-limited version of the image, and show representative results for a sparse aperture imaging application.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael C. Roggemann, David W. Tyler, "Iterative model-based image reconstruction using a constrained least-squares technique", Proc. SPIE 2827, Digital Image Recovery and Synthesis III, (25 October 1996); doi: 10.1117/12.255080; https://doi.org/10.1117/12.255080
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