Restoring images using a feed-forward neural network and the gradient adaptive learning rate algorithm
Adel Y. E. Atta, Hani M. Haarb · Journal of Modern Optics · 1998
The problem of restoring high-quality images from degraded imaging systems without any prior knowledge of the degradation model and statistics of noise is considered. A novel training algorithm, the gradient adaptive learning rate, is employed to train a feedforward neural network, the nonlinear restoration model. A linearization model of the neuron's sigmoidal activation function is utilized to speed up the convergence of the algorithm. Restoration is accomplished by making use of the generalization capabilities of the network. Ideal data sets representing flat and edge regions that would be occurring in real images have been proposed to train the network while real images have been used only during operation of the system. Computer simulation examples are given to illustrate the significance of this method. Comparison with one of the conventional restoration methods is also presented. Simulation results indicate better performance of the proposed method to other competing methods.