Abstract
This paper treats the restoration problem of degraded and noisy image. In order to keep the image structures unaltered, an adaptive regularization scheme is employed that allows better compromise between the inversion degradation process and the smoothing. The inversion process is achieved by means the modified Hopfield neural network. Moreover, the smoothing operation is accomplished in the wavelets basis by using the à trou algorithm. A multiresolution support is deduced, and combined with a statistics analysis, for computing the adaptive regularization, in which, each scale (sub-image) is assigned to one regularization parameter according to a spatial activity of the pixels which constitute it.
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Ghennam, S., Benmahammed, K. (2002). Multiresolution Support for Adaptive Image Restoration Using Neural Networks. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_194
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DOI: https://doi.org/10.1007/3-540-46084-5_194
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