Adaptive image fusion based on nonsubsampled contourlet transform

Multiresolution-based image fusion has been the focus of considerable research attention in recent years with a number of algorithms proposed. In most of the algorithms, however, the parameter configuration is usually based on experience. This paper proposes an adaptive image fusion algorithm based on the nonsubsampled contourlet transform (NSCT), which realizes automatic parameter adjustment and gets rid of the adverse effect caused by artificial factors. The algorithm incorporates the quality metric of structural similarity (SSIM) into the NSCT fusion framework. The SSIM value is calculated to assess the fused image quality, and then it is fed back to the fusion algorithm to achieve a better fusion by directing parameters (level of decomposition and flag of decomposition direction) adjustment. Based on the cross entropy, the local cross entropy (LCE) is constructed and used to determine an optimal choice of information source for the fused coefficients at each scale and direction. Experimental results show that the proposed method achieves the best fusion compared to three other methods judged on both the objective metrics and visual inspection and exhibits robust against varying noises.