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Multimodal sensor medical image fusion based on mutual‐structure for joint filtering using sparse representation
Author(s) -
Li Weisheng,
Xu Xiaofan,
Du Jiao
Publication year - 2018
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22251
Subject(s) - sparse approximation , artificial intelligence , computer science , mutual information , image fusion , fuse (electrical) , image (mathematics) , contrast (vision) , representation (politics) , computer vision , pattern recognition (psychology) , joint (building) , fusion , image registration , architectural engineering , linguistics , philosophy , politics , law , political science , electrical engineering , engineering
Multimodal sensor medical image fusion has been widely reported in recent years, but the fused image by the existing methods introduces low contrast information and little detail information. To overcome this problem, the new image fusion method is proposed based on mutual‐structure for joint filtering and sparse representation in this article. First, the source image is decomposed into a series of detail images and coarse images by mutual‐structure for joint filtering. Second, sparse representation is adopted to fuse coarse images and then local contrast is applied for fusing detail images. Finally, the fused image is reconstructed by the addition of the fused coarse images and the fused detail images. By experimental results, the proposed method shows the best performance on preserving detail information and contrast information in the views of subjective and objective evaluations.