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- W967874151 abstract "There are practical advantages for considering the image restoration or superresolution problem in terms of a neural network formalism. An advantage that has been found is the improved performance with respect to ill-conditioning difficulties. There is a large body of empirical evidence that the neural network approach enlarges the basins of attraction of the energy function minima, thus, enhancing the chances of finding better solutions and making the final solution less dependent on the starting parameters. This chapter explains the way in which both binary (two-state) and nonbinary image reconstruction algorithms can be implemented on very similar (Hopfield) neural architectures. The image restoration algorithms discussed in the chapter were originally aimed at achieving performance beyond the diffraction limit, but are in fact capable of compensating simultaneously or separately for aberrations induced by the optical components and for the limitations of the detector. The chapter also describes some image restoration or superresolution algorithms that can be implemented on an artificial neural network. Image restoration methods are well known to be illconditioned, hence, there is the need to employ regularization techniques." @default.
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- W967874151 date "1993-01-01" @default.
- W967874151 modified "2023-09-24" @default.
- W967874151 title "Image Restoration on the Hopfield Neural Network" @default.
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- W967874151 doi "https://doi.org/10.1016/s0065-2539(08)60015-3" @default.
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