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In this paper we investigate how the method of convex projections for image restoration behaves in the presence of noise. We also introduce and test a new noise-smoothing procedure in which the restored image is forced to lie within a certain L_2 distance of the noisy data. We show that, in the presence of noise, restoration by convex projections is superior to the Gerchberg-Papoulis method.

An optical–digital approach to the problem of automatic detection of dents and scratches on specular or semispecular surfaces is presented. The method uses the optical reflecting properties of dents and scratches to locate and detect them. Dents are found by a grid projection and encoding technique; scratches are found by using the property that diffuse reflectors scatter over a wider angle (Lambert’s law) than specular ones and, therefore, seem brighter when viewed at certain angles. The procedure for finding dents and scratches was found to be very robust: dents as small as 1% of the viewed field and 0.0053 cm (0.0021 in.) deep and scratches as small as 0.003 cm (0.001 in.) wide and 0.00064 cm (0.00025 in.) deep and of variable minimum length were easily detected using nonspecially prepared samples.

In this paper we consider the problem of detecting a target regardless of its orientation when it is known that the target must be from one of two classes. We assume significant random intraclass variability, a complication which requires techniques from statistical pattern recognition for amelioration. The Foley-Sammon transformation for selecting optimum features from random training samples is used to solve the problem.