Image Fusion Algorithm Based on Nonsubsampled Dual-tree Complex Contourlet Transform and Compressive Sensing Pulse Coupled Neural Network

To overcome the shortages of image fusion method based on traditional wavelet transform, a novel image fusion algorithm based on non-subsampled dual-tree complex contourlet transform（NSDTCT） and compressive sensing pulse coupled neural network（CS-PCNN） is proposed. Firstly, decompose the source images by NSDTCT to obtain the low frequency sub-band coefficients and high frequency sub-band coefficients. For the low frequency sub-band coefficients, an adaptive weighted fusion method combining the regional average gradient, regional energy with Sigmoid function is presented. For the high frequency sub-band coefficients with large amount of data, a fusion rule based on the theory of CS-PCNN is presented, and the novel sum-modified Laplacian is used for the external input of PCNN. Finally, the fused image is obtained by performing the inverse NSDTCT on the fused coefficients. The experimental results show that the proposed algorithm can improve the computation efficiency and the quality of the fused image, and outperforms other classical fusion algorithms in terms of both visual quality and objective evaluation.