Multiresolution support applied to image filtering and restoration
Graphical Models and Image Processing
Digital Image Processing
Image processing through multiscale analysis and measurementnoise modeling
Statistics and Computing
Image Restoration Using Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
IEEE Transactions on Signal Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
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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 脿 trou algorithm. A multiresolution support is deduced, and combined with a statistics analysis, for computing the adaptive regularization, in which, each scale (subimage) is assigned to one regularization parameter according to a spatial activity of the pixels which constitute it.