Application of improved BPNN in image restoration-learning coefficient

A new method of artificial intelligence based on a new improved back propagation neural network (BPNN) algorithm is partially applied in the problem of image restoration. In order to overcome the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc., different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The method applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in improving the efficiency overall both in visual effect and quality analysis.