Semi-Blind Image Restoration using a Local Neural Approach

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.