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An artificial neural network for real-time image restoration

Today optical measuring devices are used in many applications. The measurement accuracy should be very good. But when operating with image signals, irregularities of the scanning system must often be corrected. Blur, geometric distortion and unequal brightness distribution can lead to difficulties during further processing of an image. In the following, it is shown how an artificial neural network can be applied to image restoration. In order to calibrate the correcting system the weights of the neural network are trained. Using suitable training patterns and an appropriate optimization criterion for the degraded images, in the result the dimensioned network represents a space variant filter with a behavior similar to the well-known Wiener filter. A pipeline processor simulates a neural network operating in real time. Theoretical considerations and experimental results are given in this paper.