Combined the advantages of time-frequency separation of complex shearlet (CST) with the feature of guided filtering, a new image fusion algorithm based on CST domain and guided filtering is proposed. Firstly, CST is utilized for decomposition of the source images. Secondly, two scale guided filtering fusion rule is applied to the low frequency coefficients. Thirdly, larger sum-modified-Laplacian with guided filtering fusion rule is applied to the high frequency coefficients. Finally, the fused image is gained by the inverse CST. The algorithm can not only preserve the information of the source images well, but also improve the spatial continuity of fusion image. Experimental results show that the proposed method is superior to other current popular ones both in subjective visual and objective performance.
To get a better fused performance in the multi-focus image fusion based on a transform domain, a new multi-focus image algorithm combined with the adaptive dual-channel spiking cortical model (SCM) in non-subsampled shearlet (NSST) domain and the difference images is proposed in this paper. First, a basic fused image is constructed in the NSST domain by registering the source image and adaptive dual channel SCM (dual-channel SCM). Next, the focus areas of the sources input images based on the difference images between the basic fused image and the sources images are detected. In the end, the final fused image generated in this paper is realized by combining the focal regions. Because of the global coupling of the dual-SCM, the synchronization characteristics of the pulse, and the multi-resolution and direction of the NSST, the proposed algorithm can preserve the information of the source's image well and present a clear image more in line with the human visual effects. In summary, the image fusion algorithm that we have designed is superior to the most advanced algorithms. INDEX TERMS Difference images, multi-focus image fusion, non-subsampled shearlet, spiking cortical model.
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