Bayesian Methods in Nonlinear Digital Image Restoration
- 1 March 1977
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Computers
- Vol. C-26 (3) , 219-229
- https://doi.org/10.1109/tc.1977.1674810
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.Keywords
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