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29 December 1992 Regularized image reconstruction using neural networks
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Iterative methods have long been studied in order to reconstruct images from limited noisy spectral data or low pass filtered noisy images; they rely on minimizing a well-defined energy function. Such methods can be implemented on Hopfield neural networks, as a direct result of comparing energy function parameters. Consequently, a fully parallel (neural) processor can be programmed to implement a reconstruction algorithm. We have studied the properties of these neural solutions and show that they provide a regularized and apodized result with some attractive and interesting properties.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ronald J. Steriti and Michael A. Fiddy "Regularized image reconstruction using neural networks", Proc. SPIE 1767, Inverse Problems in Scattering and Imaging, (29 December 1992);

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