Bayesian Methods in Nonlinear Digital Image Restoration

Authors:
B. R. Hunt
Affiliations:
Department of Systems and Industrial Engineering, University of Arizona
Venue:
IEEE Transactions on Computers
Year:
1977

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Abstract

Prior techniques in digital image restoration have assumed linear relations between the original blurred image intensity, the silver density recorded on film, and the film-grain noise. In this paper a model is used which explicitly includes nonlinear relations between intensity and film density, by use of the D-log E curve. Using Gaussian models for the image and noise statistics, a maximum a posteriori (Bayes) estimate of the restored image is derived. The MAP estimate is nonlinear, and computer implementation of the estimator equations is achieved by a fast algorithm based on direct maximization of the posterior density function. An example of the restoration method implemented on a digital image is shown.