NonSubsampled Contourlet Transform Combined with Energy Entropy for Remote Sensing Image Fusion

A remote sensing image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and energy entropy is proposed in this paper. NSCT has characteristics of good multiresolution, shift-invariance and high directionality. It can give an asymptotic optimal representation of edges and contours in image. The local energy is robust in the representing and locating of all kinds of image features. In this paper, NSCT is used to perform a multi-scale decomposition to a remote image. Secondly, the local energy and the local energy entropy of the high and low frequency-coefficients are calculated separately. Then we choose new coefficients based on the weighting coefficients, which is calculated by energy entropy. Finally, the fused image is generated by reverse NSCT. Compared with wavelet transform, contourlet transform and NSCT, the issue regarding evaluation of fusion result is also discussed. Some image fusion examples illustrate that the proposed algorithm in this paper gets richer information of direction and has great robustness to noise.