An edge preserving regularization model for image restoration based on hopfield neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Neuro fuzzy and punctual kriging based filter for image restoration
Applied Soft Computing
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When using a regularized approach for image restora-tionthere is always a compromise between image sharpnessand noise suppression. Therefore, the main problem is toremove as much noise as possible while preserving sharpnessin the restoration. To this cause we introduce a spa-tiallyregularized neural approach that makes use of localimage statistics to apply varying regularization to differentareas of the image. This is achieved with an efficient parallelimplementation of the Hopfield neural network. Theproposed approach exhibits an improvement in restorationquality and execution time over the existing approaches.This is illustrated on simulations on benchmark images.