Image fusion based on non-sampling shearlets and weighted area feature

To improve the remote sensing image and multi-focus image fusion accuracy, combined with the non-sampling shearlets transform (NSST) which can capture the details of the image features, we proposed a image fusion method based on NSST and weighted area feature. Firstly this method uses nonsampling shearlets transform for source image to carry on multi-scale multi-direction decomposition to get low-frequency and high-frequency subbands.Then the improved gradient projection of non-negative matrix factorization(NMF) is used for the low-frequency sub-band coefficient,while the high frequency sub-band coefficient uses the fusion strategy combining the regional energy and variance of the weighted area. Finally, the non-sampling shearlets inverse transformation is used to get image fusion. The experimental results show that this method can well retain the useful information of multiple images from the aspect of subjective vision,and the comparison results are given with other fusion methods from the aspect of objective evaluation indexes such as entropy, mutual information and weighted edge information preservation values.