Image Restoration from Multiple Sources

This paper proposes a new method of image restoration. The proposed method allows to combine information from several sources, taking the perceived credibility of each into account. It is applicable to both ordinal (e.g., gray level image) and non-ordinal (e.g., classified forest map) categorized images. The accuracy checks have shown the method to be robust with respect to the prior information and the accuracy of the sources. Two application examples are provided

Real-Time Image Restoration with an Artificial Neural Network

. 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 which frequently appear spacevariant 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, in the result 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 1-D, 2-D and 3-D implementations in soft- and hardware are given. 1. Introduction When operating with image signals, irregularities of the scann..