文章基本信息

标题：Fusion for Medical Images based on Shearlet Transform and Compressive Sensing Model
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作者：Niu Ling ; Duan Mei-Xia
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：4
页码：1-10
DOI：10.14257/ijsip.2016.9.4.01
出版社：SERSC
摘要：Faced with the poor ability of traditional transform domain tools to capture the image information and the high requirements on the precision and real-time of medical imaging, a novel fusion technique for medical images based on shearlet transform (ST) and compressive sensing (CS) model is proposed in this paper. Due to the better competence of image information capturing, ST is utilized to conduct the multi-scale and multi- directional decompositions of source images. In addition, the measurement matrix is adopted to realize the sparse representation of the high-frequency coefficients obtained from ST. The fusion data of high-frequency sub-images can be attained via the largest- value method. Finally, the final fused image can be obtained by using inverse ST. Compared with current typical techniques especially the non-negative matrix factorization based ones; simulation experimental demonstrates that the proposed one has remarked superiorities in terms of both subjective and objective evaluations.
关键词：shearlet transform; compressive sensing; image fusion; medical image