Image Restoration Technology Based on Discrete Neural network

Abstract

PREFACEIn human life and production activities, the majority of information acquired is visual information, such as the plate number, auto logo or model required to be identified in daily traffic. However, the role of nature often leads to incomplete information. For example, the note fonts might fade after several years, thus resulting in difficulty in obtaining commercial evidence or other activities. How to restore the incomplete information becomes a problem to be solved by modern human beings. Since the advent of the neural network, it is widely used and promoted to the image restoration technology. This paper implements the theory based on the classical mathematical theory, applies the discrete neural network in the field of image restoration, and compares the identification effect of the feedforward network. MODELING Theory of BP neural networkBP neural network refers to a kind of multilayer feedforward bionic algorithm. This algorithm has two main features: The first feature is the forward transmission of information; the second feature is the reverse transmission of error. There is no interaction between neurons. The change of values has an inherited effect, which is repeatedly recycled until reaching the desired error, and training a matrix that is in line with the expected rate of specific gravity [3] .In Figure 1, As shown in Figure 1, the reverse-transmission neural network is essentially a nonlinear function; the independent variable is an input value of the network; the dependent variable is an output value of the network, thereby building a function relation from the dimension (n) to the dimension (m). The training network can make data become standard and network more intelligent. The training steps are as follows:First step: initialize the network. Determine the number of nodes at the typing layer (n), the number of nodes at the hidden layer (l) and the number of nodes at the printing layer (m) according to the typing and printing matrix(X,Y). Initialize the specific gravity connected between the neurons at the typing layer and the printing layer ( ij Z and jk Z ), the range at the hidden layer (a), the range at the printing layer (b) and a given acquisition rate and agitation function. Second step: output at the hidden layer. Determine the number of nodes at the typing layer (n), the specific gravity connected between the hidden layers ( ij Z ) and the range (a) according to the matrix(X,Y), so as to calculate the output at the hidden layer (H). Image Restoration Technology Based on Discrete Neural network Duoying ZhouCollege of Computer and Control Engineering, Nankai University, Tianjin, China ABSTRACT: With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration proc...