Regional Feature Self-Adaptive Image Fusion Method Based on Nonsubsampled Steerable Pyramid Transform

To conquer the weakness of existing traditional image fusion method based on the steerable pyramid transform,a novel adaptive fusion algorithm of multi-sensor images based on nonsubsampled steerable pyramid transform(NSSPT) is proposed.Firstly,the NSSPT is performed on the source images with different scales and directions,thus both the low and high frequency subband coefficients together with varieties of directional bandpass subband coefficients are obtained.Secondly,for the low frequency subband coefficients,a selection principle based on the local area difference of the coefficient's mean value is presented,while for the high frequency subband coefficients and varieties of directional bandpass subband coefficients,a scheme based on the local area average energy combined with the weighted average scheme is presented,which is also consistent with the regional feature of the high and bandpass sub-images.Finally,the fused image is obtained by performing the inverse NSSPT on the combined coefficients.The experimental results show that the proposed approach not only can avoid the introduction of the artifacts and high frequency noise,but also can significantly outperform the traditional image fusion methods based on the pyramid transform,wavelet transform or steerable pyramid transform in terms of both visual quality and objective evaluation criteria.