원문정보
초록
영어
After the image decomposition with Non-subsampled Shearlet Transform (NSST), the transform coefficients have a larger redundancy. In order to reduce the redundant information, an image fusion algorithm based on NSST and non-negative matrix factorization (NMF) is introduced. The source images are decomposed with NSST into the low-frequency coefficients and the high-frequency sub-band coefficients. The low-frequency coefficients are fused based on NMF theory. The high-frequency coefficients are fused based on Regional Sum Modified-Laplacian (SML) Maximum. Finally, the inverse NSST is used to reconstruct the final fused image. The proposed algorithm can effectively remove redundant information, extract global features and capture more direction details information of multi-source image. Experiments show that the proposed algorithm has obvious advantages and the fused image quality has been greatly improved. The presented algorithm is superior to other fusion algorithms from the objective parameters.
목차
1. Introduction
2. Related Work
2.1. Non-subsampled Shearlet Transform
2.2. Theory of Non-negative Matrix Factorization
3. An Algorithm of Medical Image Fusion Based on NSST and NMF
3.1. Fusion Frame
3.2. The Fusion Rule of the Low Frequency Coefficients
3.3. The Fusion Rule of the High Frequency Coefficients
4. Experimental Results and Discussion
5. Conclusion
Acknowledgements
References