Research on Real-Time Image Sharpening Methods Based on Optimized Neural Network

In order to resolve the contradiction between computing performance and accuracy of the traditional neural network with continuous weights, and its characteristic tidy memory capacity in embedded systems, a neural network optimization method is proposed. Firstly, we represent the weights of neural network with integers and train the neural network using the genetic algorithm. Secondly, the continuous nonlinear-activation function of the neuron is transformed into discrete and linear function using the least-squares arithmetic. Then, the optimized neural network is applied to the image sharpening for verifying its feasibility. Results of experiment show that the new method has a good real time capability and effect in hardware.