A Novel Visible Image Fusion Algorithm based on Downsampling Fractional Wavelet Transform and Shearlet Transform

Due to the disadvantage of traditional Shearlet transform fusion method, namely, Shearlet transform may emerge Gibbs phenomenon and exist large-scale image data, and in order to suppress such pseudo-Gibbs effect in the process of downsampling, we propose a novel method based on downsampling fractional wavelet transform and Shearlet transform. Our proposed method combines the theory of compressed sensing with decomposition of the different gray distribution feature source images. In our paper, the comparison experimental results are conducted respectively by using image fusion rules of regional maximum value and variance matching. The results show that our method can avoid the pseudo-Gibbs phenomenon, compared with traditional method, our method reduces the amount of data transmission and fusion, enhancing the fusion efficiency, and also shortens the image fusion time to no more than 50s.