Medical Image Fusion Based on Guided Filtering and Sparse Representation

In order to improve the clarity of fused images, a multi-modal medical image fusion algorithm based on guided filter and adaptive sparse representation is proposed. Specifically, this algorithm adopts Gaussian filter to decompose the input images into detail layers and base layers. Subsequently, the weight maps of base layers are obtained based on saliency characteristics and guided filters, which are further utilized to fuse the base layers in combination with the weighted average rule; at the same time, the detail layers are fused through an adaptive sparse representation algorithm. Finally, the fused layers are directly added into the base layers to obtain the fused image. This algorithm is compared with other six classical algorithms on quality evaluation and visual analysis. In addition, the time complexity of the algorithm is also compared with that of two sparse representation-based algorithms. The results show that this algorithm outperforms other algorithms in the preservation of texture and edge information. Meanwhile, its time complexity is significantly better than that of sparse representation-based algorithms.