Digital Image Processing
Digital Image Restoration
Digital Image Restoration
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Weight assignment for adaptive image restoration by neural networks
IEEE Transactions on Neural Networks
Semi-blind image restoration using a local neural approach
Neurocomputing
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This work aims to define and experimentally evaluate an iterative strategy based on neural learning for semi-blind image restoration in the presence of blur and noise. A salient aspect of our solution is the local estimation of the restored image based on gradient descent strategies. This method can be viewed as a neural strategy where the pixels of the restored image are the synapse's weights that the neural network tries to modify during learning to minimize the output error measure; the learning strategy adopted is unsupervised. The method was evaluated experimentally using a test pattern generated by a checkerboard function in Matlab. To investigate whether the strategy can be considered an alternative to conventional restoration procedures, the results were compared with those obtained by a well known neural restoration approach.