Blind deconvolution by self-organization

In this paper, we devise a self-organizing network to solve both the unknown system and unknown input in blind deconvolution of blurred images. We utilize a criterion function which has a similar form as the Kullback-Leibler cross information formula to adapt the network´s weights to approach the unknown system function. This adaptation gradually reduces the criterion value which is a distance measure between the system output and the output of the adapted system with a reconstructed input signal. The weight matrices of the neurons in the network are shifted versions of the system function and will be aligned in the network according to their shifts during convergence. This is because the convolution operation which copes with this network scheme and the hidden topology of the shifted system functions can be aligned similarly in a 2D plane