1 |
D. Ravi, A.B. Szczotka, S.P. Pereira, T. Vercauteren, Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy, Med. Image Anal. 53 (2019) 123-131.
DOI
|
2 |
T. Mamyrbayev, K. Ikematsu, P. Meyer, A. Ershov, A. Momose, J. Mohr, Super-resolution scanning transmission X-ray imaging using single biconcave parabolic refractive lens array, Sci. Rep. 9 (2019), https://doi.org/10.1038/s41598-019-50869-8.
DOI
|
3 |
L. Xu, X. Zeng, Z. Huang, W. Li, H. Zhang, Low-dose chest X-ray image super-resolution using generativeadversarial nets with spectral normalization, Biomed. Signal Process Contr. 55 (2020), 101600.
DOI
|
4 |
N. Nguyen, P. Milanfar, G. Golub, Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement, IEEE Transsanctions on Image Processing 10 (2001) 1299-1308.
DOI
|
5 |
S.C. Park, M.K. Park, M.G. Kang, Super-resolution image reconstruction: a technical overview, IEEE Signal Process. Mag. (2003) 21-36.
|
6 |
M. Irani, S. Peleg, Improving resolution by image registration, CVGIP Graph. Models Image Process. 53 (1991) 231-239.
DOI
|
7 |
S. Farsiu, M.D. Robinson, M. Elad, P. Milanfar, Fast and robust multiframe super resolution, IEEE Transsanctions on Image Processing 13 (2004) 1327-1344.
DOI
|
8 |
Z. Lin, H. Shum, Fundamental limits of reconstruction-based superresolution algorithms under local translation, IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 83-97.
DOI
|
9 |
S. Pujals, N. Feiner-Gracia, P. Delcanale, I. Voets, L. Albertazzi, Super- resolution microscopy as a powerful tool to study complex synthetic materials, Nature Reviews Chemistry 3 (2019) 68-84.
DOI
|
10 |
J. Sun, J. Sun, Z. Xu, H. Shum, Image super-resolution using gradient profile prior, in: 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 23-28.
|
11 |
D. Glasner, S. Bagon, M. Irani, Super-resolution from a single image, in: 2009 IEEE 12th International Conference on Computer Vision, 2009, https://doi.org/10.1109/ICCV.2009.5459271.
DOI
|
12 |
J. Yang, Z. Wang, Z. Lin, S. Cohen, T. Huang, Coupled dictionary training for image super-resolution, IEEE Transsanctions on Image Processing 21 (2012) 3467-3478.
DOI
|
13 |
J. Kim, J.K. Lee, K.M. Lee, Accurate image super-resolution using very deep convolutional networks, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 1646-1654.
|
14 |
J. Yang, Z. Lin, S. Cohen, Fast image super-resolution based on in-place example regression, in: 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2013, pp. 1059-1066.
|
15 |
J. Huang, A. Singh, N. Ahuja, Single image super-resolution from transformed self-exemplars, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2015, pp. 5197-5206.
|
16 |
C. Dong, C.C. Loy, K. He, X. Tang, Image super-resolution using deep convolutional networks, IEEE Trans. Pattern Anal. Mach. Intell. 38 (2016) 295-307.
DOI
|
17 |
Z.H. Cho, Y.D. Son, H.K. Kim, K.N. Kim, S.H. Oh, J.Y. Han, I.K. Hong, Y.B. Kim, A fusion PET-MRI system with a high-resolution research tomograph-PET and ultra-high field 7.0 T-MRI for the molecular-genetic imaging of the brain, Proteomics 8 (2008) 1302-1323.
DOI
|
18 |
Y.J. Lee, S.J. Park, S.W. Lee, D.H. Kim, Y.S. Kim, H.J. Kim, Comparison of photon counting and conventional scintillation detectors in a pinhole SPECT system for small animal imaging: Monte Carlo simulation studies, J. Kor. Phys. Soc. 62 (2013) 1317-1322.
DOI
|
19 |
S. Abbaspour, B. Mahmoudian, J.P. Islamian, Cadmium telluride semiconductor detector for improved spatial and energy resolution radioisotopic imaging, World J. Nucl. Med. 16 (2017) 101-107.
DOI
|
20 |
G. Huang, Z. Liu, L.V.D. Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 2261-2269.
|
21 |
S. Sohn, H.J. Shi, S.H. Wang, S.K. Lee, S.Y. Park, J.S. Lee, J.S. Eom, Mycobacterium avium complex infection-related immune reconstitution inflammatory syndrome mimicking lymphoma in an human immunodeficiency virus-infected patient, Infection & Chemotherapy 50 (2018) 350-356.
DOI
|
22 |
C.R. Park, Y. Lee, Comparison of PET image quality using simultaneous PET/MR by attenuation correction with various MR pulse sequences, Nuclear Engineering and Technology 51 (2019) 1610-1615.
DOI
|
23 |
D.W. Townsend, Multimodality imaging of structure and function, Phys. Med. Biol. 53 (2008) R1-R39.
DOI
|
24 |
C.T. Yang, K.K. Ghosh, P. Padmanabhan, O. Langer, J. Liu, D.N.C. Eng, C. Halldin, B. Gulyas, PET-MR and SPECT-MR multimodality probes: development and challenges, Theranostics 8 (2018) 6210-6232.
DOI
|
25 |
P. Lecoq, Development of new scintillators for medical applications, Nucl. Instrum. Methods Phys. Res. 809 (2016) 130-139.
DOI
|
26 |
F.P. Jansen, J.L. Vanderheyden, The future of SPECT in a time of PET, Nucl. Med. Biol. 34 (2007) 733-735.
DOI
|
27 |
H.H. Li, J.R. Votaw, Optimization of PET activation studies based on the SNR measured in the 3-D hoffman brain phantom, IEEE Trans. Med. Imag. 17 (1998) 596-605.
DOI
|
28 |
D.W. Townsend, Positron emission tomography/computed tomography, Semin. Nucl. Med. 38 (2008) 152-166.
DOI
|
29 |
R. Fattal, Image upsampling via imposed edge statistics, ACM Trans. Graph. 26 (2007), 95-1-8.
DOI
|
30 |
W.T. Freeman, E.C. Pasztor, O.T. Carmichael, Learning low-level vision, Int. J. Comput. Vis. 40 (2000) 25-47.
DOI
|
31 |
T. Tong, G. Li, X. Liu, Q. Gao, Image super-resolution using dense skip connections, in: 2017 IEEE International Conference on Computer Vision, CVPR, 2017, pp. 4809-4817.
|
32 |
K. Kim, M.H. Lee, Y. Lee, Investigation of a blind-deconvolution framework after noise reduction using a gamma camera in nuclear medicine imaging, Nuclear Engineering and Technology 52 (2020) 2594-2600.
DOI
|
33 |
P. Zanzonico, Principles of nuclear medicine imaging: planar, SPECT, PET, multi-modality, and autoradiography systems, Radiat. Res. 177 (2012) 349-364.
DOI
|
34 |
D.J. Rowland, S.R. Cherry, Small-animal preclinical nuclear medicine instrumentation and methodology, Semin. Nucl. Med. 38 (2008) 209-222.
DOI
|
35 |
J. Kim, J.K. Lee, K.M. Lee, Deeply-recursive convolutional network for image super-resolution, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 1637-1645.
|
36 |
D. P. Kingma, J. Ba, Adam: a Method for Stochastic Optimization, arXiv preprint (2014) arXiv:1412.6980.
|
37 |
S.Y. Chun, J.A. Fessler, Y.K. Dewaraja, Post-reconstruction non-local means filtering methods using CT side information for quantitative SPECT, Phys. Med. Biol. 58 (2013) 6225-6240.
DOI
|
38 |
S. Baker, T. Kanade, Limits on super-resolution and how to break them, IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 1167-1183.
DOI
|
39 |
P. Russo, F.D. Lillo, V. Corvino, P.M. Frallicciardi, A. Sarno, G. Mettivier, CdTe compact gamma camera for coded aperture imaging in radioguided surgery, Phys. Med. 69 (2020) 223-232.
DOI
|
40 |
W.T. Freeman, T.R. Jones, E.C. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications 22 (2002) 56-65.
|
41 |
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 2472-2481.
|
42 |
M. Koutalonis, H. Delis, G. Spyrou, L. Costaridou, G. Tzanakos, G. Panayiotakis, Contrast-to-noise ratio in magnification mammography: a Monte Carlo study, Phys. Med. Biol. 52 (2007) 3185-3199.
DOI
|
43 |
O.M. Rijal, H. Ebrahimian, N.M. Noor, Determining features for discriminating PTB and normal lungs using phase congruency model, in: Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012, 2012, pp. 341-344.
|
44 |
L. Yu, X. Zhang, Y. Chu, Super-resolution reconstruction algorithm for infrared image with double regular items based on sub-pixel convolution, Appl. Sci. 10 (2020), https://doi.org/10.3390/app10031109.
DOI
|
45 |
J.T. Dobbins III, E. Samei, N.T. Ranger, Y. Chen, Intercomparison of methods for image quality characterization. II. Noise power spectrum, Med. Phys. 33 (2006) 1466-1475.
DOI
|
46 |
M. Elad, A. Feuer, Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images, IEEE Transsanctions on Image Processing 6 (1997) 1646-1658.
DOI
|
47 |
S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint (2015) arXiv:1502.03167.
|