Nonlinear restoration of spatially varying blurred images using self-organizing neural network

An efficient nonlinear restoration of spatially varying blurred images with noise is presented using a self-organizing neural network (SONN). The proposed method can effectively restore the blurred images by using the region classification and the learning property of SONN adapted for the blur sensitivity of the receptive field. In addition, receptive fields are adaptively overlapped to eliminate the block effect within the restored images. The proposed method eliminates the need to calculate the gradient, gradient step size, or Hessian of error surface, which affect the performance of the least squares method or of the constraint optimization. Simulation results for the space-variant blurred pepper image show the performance improvement of about 4.86 dB or 3.57 dB, as compared to that of the Richardson-Lucy algorithm or that of conventional neural networks, respectively.