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Medical Image Fusion Based on Sparse Representation and Guided Filtering
| Content Provider | Scilit |
|---|---|
| Author | Huang, Sa Chu, Guangyu Fei, Yifan Zhang, Xiaoli Wang, Hailiang |
| Copyright Year | 2019 |
| Description | Journal: Journal of Physics: Conference Series In this paper, we proposed a medical image fusion algorithm based on sparse representation and guided filtering. One of attractive features in the algorithm is that it can preserve the structural information of structural image and color information of functional image. We use a sparse representation in low frequency, initialize the dictionary by using DCT transform, and train the dictionary with each input source image as a training example. It not only ensures time complexity, but also ensures that the low-frequency fusion rules are adaptive. At high frequencies, we use the method of guided filtering to extract structural information from high-frequency images, and use the injection method to fuse high-frequency sub-bands to ensure the validity and richness of structural information. Experimental results show that the proposed fusion algorithm is superior to comparative algorithms in terms of subjective and objective evaluation methods. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1302/2/022045/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1302/2/022045 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 2 |
| Volume Number | 1302 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-08-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Industrial Engineering Sparse Representation High Frequency Guided Filtering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
