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Detection of low-contrast images in film-grain noise

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Abstract

When low contrast photographic images are digitized by a very small aperture, extreme film-grain noise almost completely obliterates the image information. Using a large aperture to average out the noise destroys the fine details of the image. In these situations conventional statistical restoration techniques have little effect, and well chosen heuristic algorithms have yielded better results. In this paper we analyze the noise-cheating algorithm of Zweig, et al. [ J. Opt. Soc. Am. 65, 1347 ( 1975)] and show that it can be justified by classical maximum-likelihood detection theory. A more general algorithm applicable to a broader class of images is then developed by considering the signal-dependent nature of film-grain noise. Finally, a Bayesian detection algorithm with improved performance is presented.

© 1978 Optical Society of America

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