关键词: 图像融合, 脉冲耦合神经网络, 小波变换, 拉普拉斯能量, 点火映射图

Abstract: Concerning the fusion problem of multi-focus image with the same scene, an algorithm of image fusion based on wavelet transform and adaptive Pulse Coupled Neural Network (PCNN) was proposed. Firstly, original images were decomposed by wavelet transform, and the sub-band images at different scales were obtained. Secondly, a fusion rule was given through making use of synchronous pulse bursts. This method used Energy of Laplacian (EOL) of wavelet coefficients at different scales as the linking strength of the corresponding neuron. After the processing of PCNN with the adaptive strength, new fire mapping images in wavelet domain were obtained. According to the fire mapping images, the fusion coefficients were decided by the compare-select operator, and then the region consistency test was used on the fusion coefficients to obtain the final fusion coefficients. Finally, fusion images were obtained by wavelet inverse transform. The experimental results illustrate that this algorithm is efficient to extract feature information from the original images and improves fusion images. It outperforms the conventional methods in subjective vision effect and objective performance index.

Key words: image fusion, Pulse Coupled Neural Network (PCNN), wavelet transform, Energy of Laplace (EOL), fire mapping image