Variational image fusion approach based on TGV and local information
In this study, the authors propose a variational approach based on total generalised variation (TGV) and local gradient information to fuse multi‐focus images as well as medical images of computed tomography and magnetic resonance. They use the second‐order TGV as the regularisation term and local g...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-06-01
|
| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2017.0451 |
| _version_ | 1797684834865250304 |
|---|---|
| author | Qinxia Wang Xiaoping Yang |
| author_facet | Qinxia Wang Xiaoping Yang |
| author_sort | Qinxia Wang |
| collection | DOAJ |
| description | In this study, the authors propose a variational approach based on total generalised variation (TGV) and local gradient information to fuse multi‐focus images as well as medical images of computed tomography and magnetic resonance. They use the second‐order TGV as the regularisation term and local gradient information as the fusion weight to extract image features. To compute the new model effectively, the primal‐dual algorithm is carried out. Various experiments are made to verify the effectiveness of the proposed methods. |
| first_indexed | 2024-03-12T00:36:31Z |
| format | Article |
| id | doaj.art-c27d92175f7c46c3bc22bf1e0899bf57 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:36:31Z |
| publishDate | 2018-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-c27d92175f7c46c3bc22bf1e0899bf572023-09-15T09:36:27ZengWileyIET Computer Vision1751-96321751-96402018-06-0112453554110.1049/iet-cvi.2017.0451Variational image fusion approach based on TGV and local informationQinxia Wang0Xiaoping Yang1School of ScienceNanjing University of Science & TechnologyNanjing210094JiangsuPeople's Republic of ChinaDepartment of MathematicsNanjing UniversityNanjing210093JiangsuPeople's Republic of ChinaIn this study, the authors propose a variational approach based on total generalised variation (TGV) and local gradient information to fuse multi‐focus images as well as medical images of computed tomography and magnetic resonance. They use the second‐order TGV as the regularisation term and local gradient information as the fusion weight to extract image features. To compute the new model effectively, the primal‐dual algorithm is carried out. Various experiments are made to verify the effectiveness of the proposed methods.https://doi.org/10.1049/iet-cvi.2017.0451variational image fusion approachtotal generalised variationlocal gradient informationcomputed tomographymedical imagesmagnetic resonance |
| spellingShingle | Qinxia Wang Xiaoping Yang Variational image fusion approach based on TGV and local information IET Computer Vision variational image fusion approach total generalised variation local gradient information computed tomography medical images magnetic resonance |
| title | Variational image fusion approach based on TGV and local information |
| title_full | Variational image fusion approach based on TGV and local information |
| title_fullStr | Variational image fusion approach based on TGV and local information |
| title_full_unstemmed | Variational image fusion approach based on TGV and local information |
| title_short | Variational image fusion approach based on TGV and local information |
| title_sort | variational image fusion approach based on tgv and local information |
| topic | variational image fusion approach total generalised variation local gradient information computed tomography medical images magnetic resonance |
| url | https://doi.org/10.1049/iet-cvi.2017.0451 |
| work_keys_str_mv | AT qinxiawang variationalimagefusionapproachbasedontgvandlocalinformation AT xiaopingyang variationalimagefusionapproachbasedontgvandlocalinformation |