1 |
RIVIERE P J LA, VARGAS P, FU G, et al. Accelerating X-ray fluorescence computed tomography [C]// Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2009: 1000-1003. 10.1109/iembs.2009.5333568
|
2 |
PELED S, YESHURUN Y. Super resolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging [J]. Magnetic Resonance in Medicine, 2001, 45(1): 29-35. 10.1002/1522-2594(200101)45:1<29::aid-mrm1005>3.0.co;2-z
|
3 |
GOSHTASBY A, TURNER D A, ACKERMAN L V. Matching of tomographic slices for interpolation [J]. IEEE Transactions on Medical Imaging, 1992, 11(4): 507-516. 10.1109/42.192686
|
4 |
GREVERA G J, UDUPA J K. Shape-based interpolation of multidimensional grey-level images [J]. IEEE Transactions on Medical Imaging, 1996, 15(6): 881-892. 10.1109/42.544506
|
5 |
LEE T Y, WANG W H. Morphology-based three-dimensional interpolation [J]. IEEE Transactions on Medical Imaging, 2000, 19(7): 711-721. 10.1109/42.875193
|
6 |
朱杨兴,鲍旭东.基于非刚体配准的体数据插值重建方法[J].中国医学物理学杂志,2006,23(6):412-415,457. 10.3969/j.issn.1005-202X.2006.06.005
|
|
ZHU Y X, BAO X D. Nonrigid registration based interpolation approach [J]. Chinese Journal of Medical Physics, 2006, 23(6): 412-415, 457. 10.3969/j.issn.1005-202X.2006.06.005
|
7 |
LENG J L, XU G L, ZHANG Y J. Medical image interpolation based on multi-resolution registration [J]. Computers and Mathematics with Applications, 2013, 66(1): 1-18. 10.1016/j.camwa.2013.04.026
|
8 |
HERMAN G T, ROWLAND S W, YAU M M. A comparative study of the use of linear and modified cubic spline interpolation for image reconstruction [J]. IEEE Transactions on Nuclear Science, 1979, 26(2): 2879-2894. 10.1109/tns.1979.4330555
|
9 |
LEHMANN T M, GONNER C, SPITZER K. Survey: interpolation methods in medical image processing [J]. IEEE Transactions on Medical Imaging, 1999, 18(11): 1049-1075. 10.1109/42.816070
|
10 |
ZHANG C, LI W, TRAVIS D. Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach [J]. International Journal of Remote Sensing, 2007, 28(22): 5103-5122. 10.1080/01431160701250416
|
11 |
FERRAND G, LUONG M, CLOOS M A, et al. Accelerating parallel transmit array B1 mapping in high field MRI with slice undersampling and interpolation by kriging [J]. IEEE Transactions on Medical Imaging, 2014, 33(8): 1726-1734. 10.1109/tmi.2014.2322440
|
12 |
CARR J C, FRIGHT W R, BEATSON R K. Surface interpolation with radial basis functions for medical imaging [J]. IEEE Transactions on Medical Imaging, 1997, 16(1): 96-107. 10.1109/42.552059
|
13 |
PENNEY G P, SCHNABEL J A, RUECKERT D, et al. Registration-based interpolation [J]. IEEE Transactions on Medical Imaging, 2004, 23(7): 922-926. 10.1109/tmi.2004.828352
|
14 |
FRAKES D H, DASI L P, PEKKAN K, et al. A new method for registration-based medical image interpolation [J]. IEEE Transactions on Medical Imaging, 2008, 27(3): 370-377. 10.1109/tmi.2007.907324
|
15 |
SCHLEMPER J, CABALLERO J, HAJNAL J V, et al. A deep cascade of convolutional neural networks for dynamic MR image reconstruction [J]. IEEE Transactions on Medical Imaging, 2018, 37(2): 491-503. 10.1109/tmi.2017.2760978
|
16 |
RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation [C]// Proceedings of the 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS9351. Cham: Springer, 2015: 234-241.
|
17 |
LIU S Q, XU D G, ZHOU S K, et al. 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes [C]// Proceedings of the 2018 International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS11071. Cham: Springer, 2018: 851-858.
|
18 |
DONG C, LOY C C, HE K M, et al. Image super-resolution using deep convolutional networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307. 10.1109/tpami.2015.2439281
|
19 |
KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 1646-1654. 10.1109/cvpr.2016.182
|
20 |
DONG C, LOY C C, TANG X O. Accelerating the super-resolution convolutional neural network [C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS9906. Cham: Springer, 2016: 391-407.
|
21 |
ZHANG Y L, TIAN Y P, KONG Y, et al. Residual dense network for image super-resolution [C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 2472-2481. 10.1109/cvpr.2018.00262
|
22 |
晋银峰,朱金秀,吴文霞,等.基于GAN和TV正则化的MRI超分辨率重建算法[J].计算机工程与设计,2019,40(3):767-773.
|
|
JIN Y F, ZHU J X, WU W X, et al. Total variation regularized MRI super-resolution algorithm with generative adversarial networks [J]. Computer Engineering and Design, 2019, 40(3): 767-773.
|
23 |
LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2017: 1132-1440. 10.1109/cvprw.2017.151
|
24 |
HU X C, MU H Y, ZHANG X Y, et al. Meta-SR: a magnification-arbitrary network for super-resolution [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 1575-1584. 10.1109/cvpr.2019.00167
|
25 |
PENG C, LIN W A, LIAO H F, et al. Deep slice interpolation via marginal super-resolution, fusion and refinement [EB/OL]. (2019-08-15) [2020-09-15]. . 10.1109/cvpr42600.2020.00777
|