科研之友
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摘要
Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However, most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image. This paper presents a fusion framework based on block-matching and 3D (BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform (NSCT), non-subsampled Shearlet transform (NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.
