Real-time image restoration with an artificial neural network
Gerald Krell, A. Herzog, B. Michaelis · 2002
We present a neural network that can be applied to image correction in a preprocessing unit. Blur, geometric distortion and unequal brightness distribution are typical for many scanning techniques and can lead to difficulties during further processing of an image. These and other effects of image degradation, the space-variant can be considered simultaneously by this approach. In order to calibrate the correcting system the weights of a neural network are trained. Using suitable training patterns and an appropriate optimization criterion for the degraded images, the dimensioned network represents a space-variant filter with a behavior similar to the well-known Wiener filter. The restoration result can be easily altered by the scheme of the learning data generation. Theoretical considerations and examples for 1D, 2D and 3D implementations in both software and hardware are given.