A novel wavelet image fusion algorithm based on chaotic neural network

In this paper, Transiently Chaotic Neural Network (TCNN) is used in wavelet image fusion method. This paper adopts the weighted average strategy for the fusion of the wavelet transform coefficients. The TCNN outputs the weighting coefficient of every wavelet transform pixel when the energy function of the neural network has achieved the global minimum. At the same time, the average gradient value of the region around every wavelet transform pixel gets the global maximum according to the relationship between the average gradient and energy. The wavelet transform coefficients of the fused image are got by using the weighting coefficients. The advantage of the algorithm is that the weighting coefficient is obtained through the dynamic searching optimization of the average gradient. Experiments show that the average gradient values of the fusion images using the proposed method are greater than the results using the region energy method. The TCNN method improves the performance of the fusion image effectively.