Blur identification using neural network for image restoration

A prior knowledge about the distorting operator and its parameters is of crucial importance in blurred image restoration. In this paper the continuous-valued multilayer neural network based on multivalued neurons (MLMVN) is exploited for identification of a type of blur among six trained blurs and of its parameters. This network has a number of specific properties and advantages. Its backpropagation learning algorithm does not require differentiability of the activation function. The functionality of the MLMVN is higher than the ones of the traditional feedforward neural networks and a variety of kernel-based networks. Its higher flexibility and faster adaptation to the mapping implemented make possible an accomplishment of complex problems using a simpler network. Therefore, the MLMVN can be used to solve those nonstandard recognition and classification problems that cannot be solved using other techniques.