Blur and image restoration of nonlinearly degraded images using neural networks based on modified ARMA model

In this paper, an image restoration algorithm is proposed to identify nonlinear and noncausal blur function using artificial neural networks. Image and degradation processes include both linear and nonlinear phenomena. The proposed neural network model combines an adaptive auto-associative network with a random Gaussian process, is used to restore the blurred image and blur function, simultaneously. The noisy and blurred images are modeled as nonlinear continuous associative networks, whereas auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the image degradation process. The self-organization like structure of the proposed neural network provides the potential solution of the blind image restoration problem. The estimation and restoration are implemented by using an iterative gradient based algorithm to minimize the error function.