Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (2): 584-591.DOI: 10.11772/j.issn.1001-9081.2021020219
• Multimedia computing and computer simulation • Previous Articles
Junbo LI1,2,3, Pinle QIN1,2,3, Jianchao ZENG1,2,3(), Meng LI1,2,3
Received:
2021-02-04
Revised:
2021-05-11
Accepted:
2021-05-13
Online:
2021-06-02
Published:
2022-02-10
Contact:
Jianchao ZENG
About author:
LI Junbo, born in 1996, M. S. candidate. His research interests include machine learning, computer vision, medical image analysis.Supported by:
李俊伯1,2,3, 秦品乐1,2,3, 曾建潮1,2,3(), 李萌1,2,3
通讯作者:
曾建潮
作者简介:
李俊伯(1996—),男,山西运城人,硕士研究生,主要研究方向:机器学习、计算机视觉、医学影像分析;基金资助:
CLC Number:
Junbo LI, Pinle QIN, Jianchao ZENG, Meng LI. CT three-dimensional reconstruction algorithm based on super-resolution network[J]. Journal of Computer Applications, 2022, 42(2): 584-591.
李俊伯, 秦品乐, 曾建潮, 李萌. 基于超分辨率网络的CT三维重建算法[J]. 《计算机应用》唯一官方网站, 2022, 42(2): 584-591.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021020219
卷积核量 | 补丁尺寸 | SSIM | PSNR/dB | 耗时/s |
---|---|---|---|---|
32 | 28×28 | 0.908 1 | 34.238 | 0.04 |
64 | 14×14 | 0.920 2 | 34.602 | 0.06 |
28×28 | 0.928 3 | 35.962 | ||
36×36 | 0.910 6 | 34.330 | ||
128 | 28×28 | 0.928 8 | 35.969 | 0.12 |
128 | 28×28 | 0.928 8 | 35.969 | 0.12 |
Tab. 1 Training results corresponding to selection of each parameter of the proposed model (scaling factor of 2)
卷积核量 | 补丁尺寸 | SSIM | PSNR/dB | 耗时/s |
---|---|---|---|---|
32 | 28×28 | 0.908 1 | 34.238 | 0.04 |
64 | 14×14 | 0.920 2 | 34.602 | 0.06 |
28×28 | 0.928 3 | 35.962 | ||
36×36 | 0.910 6 | 34.330 | ||
128 | 28×28 | 0.928 8 | 35.969 | 0.12 |
128 | 28×28 | 0.928 8 | 35.969 | 0.12 |
算法 | 参数量 | PSNR/dB | SSIM | ||||
---|---|---|---|---|---|---|---|
×2 | ×3 | ×4 | ×2 | ×3 | ×4 | ||
Bicubic | — | 28.337 | 25.433 | 23.181 | 0.842 1 | 0.807 4 | 0.766 9 |
Antialias | — | 28.590 | 25.540 | 23.514 | 0.861 0 | 0.829 7 | 0.791 1 |
EDSR+VMeta | 4.32×107 | 33.549 | 30.372 | 27.871 | 0.929 8 | 0.877 6 | 0.842 0 |
RDN+VMeta | 2.24×107 | 34.728 | 31.547 | 28.904 | 0.921 2 | 0.878 2 | 0.842 5 |
RCAN+VMeta | 1.61×107 | 35.146 | 31.685 | 29.330 | 0.924 6 | 0.880 1 | 0.843 7 |
DLRNet | 3.43×107 | 35.962 | 32.463 | 30.103 | 0.928 3 | 0.881 9 | 0.844 5 |
Tab. 2 PSNR and SSIM of each algorithm on test set (scaling factor of 2, 3, 4)
算法 | 参数量 | PSNR/dB | SSIM | ||||
---|---|---|---|---|---|---|---|
×2 | ×3 | ×4 | ×2 | ×3 | ×4 | ||
Bicubic | — | 28.337 | 25.433 | 23.181 | 0.842 1 | 0.807 4 | 0.766 9 |
Antialias | — | 28.590 | 25.540 | 23.514 | 0.861 0 | 0.829 7 | 0.791 1 |
EDSR+VMeta | 4.32×107 | 33.549 | 30.372 | 27.871 | 0.929 8 | 0.877 6 | 0.842 0 |
RDN+VMeta | 2.24×107 | 34.728 | 31.547 | 28.904 | 0.921 2 | 0.878 2 | 0.842 5 |
RCAN+VMeta | 1.61×107 | 35.146 | 31.685 | 29.330 | 0.924 6 | 0.880 1 | 0.843 7 |
DLRNet | 3.43×107 | 35.962 | 32.463 | 30.103 | 0.928 3 | 0.881 9 | 0.844 5 |
算法 | 参数量 | PSNR/dB | SSIM | ||||
---|---|---|---|---|---|---|---|
×2 | ×3 | ×4 | ×2 | ×3 | ×4 | ||
RDN+VM-Meta | 2.24×107 | 34.728 | 31.547 | 29.904 | 0.921 2 | 0.878 2 | 0.842 5 |
MASPP+VM-Meta | 2.37×107 | 35.181 | 31.890 | 30.243 | 0.925 3 | 0.880 0 | 0.843 8 |
CA+MASPP+VM-Meta | 2.92×107 | 35.529 | 32.211 | 30.528 | 0.926 1 | 0.880 5 | 0.844 0 |
CA+MASPP+VM-Meta | 3.04×107 | 35.643 | 32.305 | 30.590 | 0.926 6 | 0.880 8 | 0.844 0 |
CA+MASPP+VM-Meta+CRM | 3.43×107 | 35.962 | 32.563 | 30.779 | 0.928 3 | 0.881 9 | 0.844 5 |
Tab. 3 Ablation experimental on result test set
算法 | 参数量 | PSNR/dB | SSIM | ||||
---|---|---|---|---|---|---|---|
×2 | ×3 | ×4 | ×2 | ×3 | ×4 | ||
RDN+VM-Meta | 2.24×107 | 34.728 | 31.547 | 29.904 | 0.921 2 | 0.878 2 | 0.842 5 |
MASPP+VM-Meta | 2.37×107 | 35.181 | 31.890 | 30.243 | 0.925 3 | 0.880 0 | 0.843 8 |
CA+MASPP+VM-Meta | 2.92×107 | 35.529 | 32.211 | 30.528 | 0.926 1 | 0.880 5 | 0.844 0 |
CA+MASPP+VM-Meta | 3.04×107 | 35.643 | 32.305 | 30.590 | 0.926 6 | 0.880 8 | 0.844 0 |
CA+MASPP+VM-Meta+CRM | 3.43×107 | 35.962 | 32.563 | 30.779 | 0.928 3 | 0.881 9 | 0.844 5 |
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