Multi-modal medical image fusion via multi-dictionary and truncated Huber filtering

Multi-modal medical image fusion provides comprehensive and objective descriptions of lesions for clinical medical assistance. However, retaining useful information while achieving noise robustness remains challenging for existing techniques. In this paper, we propose a novel medical image fusion algorithm based on multi-dictionary convolutional sparse representation. Especially, truncated Huber filtering is first introduced to achieve detail-base layer decomposition of source images. Subsequently, multiple-dictionary decisions and nuclear energy-based rules are proposed to fuse the details and base layers, respectively. The fused image is reconstructed by synthesizing the fused detail and base components. The proposed model effectively fuses the source global structure and texture information and exhibits strong robustness against noise. Experiments involving extensive noise-free and noisy anatomical and functional medical image fusion on a public dataset covering five fusion categories demonstrate that the proposed method outperforms other state-of-the-art methods in subjective and objective evaluations. The source code of this study is publicly available at https://github.com/JEI981214/MDHU-fusion.