Name
Title | ||
|---|---|---|
A novel super-resolution CT image reconstruction via semi-supervised generative adversarial network |
Abstract | ||
|---|---|---|
Reconstruction of super-resolution CT images using deep learning requires a large number of high-resolution images. However, high-resolution images are often limited to access due to CT performance and operation factors. In this paper, a new semi-supervised generative adversarial network is presented to accurately recover high-resolution CT images from low-resolution counterparts. We use a deep unsupervised network of 16 residual blocks to design the generator and build a discriminator based on a supervised network. We also apply a parallel 1 x 1 convolution operation to reduce the dimensionality of each hidden layer's output. Four types of loss functions are presented to build a new one for enforcing the mappings between the generator and discriminator. The bulk specification layer in the commonly used residual network is removed to construct a new type of residual network. In terms of experiments, we conduct an objective and subjective comprehensive evaluation with several state-of-the-art methods. The comparison results show that our proposed network has better advantages in super-resolution image reconstruction. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00521-020-04905-8 | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Super-resolution,Computed tomography images,Residual blocks,Generative adversarial network | Journal | 32.0 |
Issue | ISSN | Citations |
SP18.0 | 0941-0643 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| X. Jiang | 1 | 19 | 6.91 |
| Mingzhe Liu | 2 | 2 | 2.73 |
| Feixiang Zhao | 3 | 1 | 0.35 |
| Xianghe Liu | 4 | 1 | 0.35 |
| Helen Zhou | 5 | 2 | 1.10 |