Super-resolution construction of intravascular ultrasound images using generative adversarial networks / 南方医科大学学报
Journal of Southern Medical University
;
(12): 82-87, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-772117
ABSTRACT
The low-resolution ultrasound images have poor visual effects. Herein we propose a method for generating clearer intravascular ultrasound images based on super-resolution reconstruction combined with generative adversarial networks. We used the generative adversarial networks to generate the images by a generator and to estimate the authenticity of the images by a discriminator. Specifically, the low-resolution image was passed through the sub-pixel convolution layer -feature channels to generate -feature maps in the same size, followed by realignment of the corresponding pixels in each feature map into × sub-blocks, which corresponded to the sub-block in a high-resolution image; after amplification, an image with a -time resolution was generated. The generative adversarial networks can obtain a clearer image through continuous optimization. We compared the method (SRGAN) with other methods including Bicubic, super-resolution convolutional network (SRCNN) and efficient sub-pixel convolutional network (ESPCN), and the proposed method resulted in obvious improvements in the peak signal-to-noise ratio (PSNR) by 2.369 dB and in structural similarity index by 1.79% to enhance the diagnostic visual effects of intravascular ultrasound images.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Blood Vessels
/
Image Processing, Computer-Assisted
/
Diagnostic Imaging
/
Image Enhancement
/
Endosonography
/
Signal-To-Noise Ratio
/
Methods
Type of study:
Diagnostic study
Language:
Chinese
Journal:
Journal of Southern Medical University
Year:
2019
Type:
Article
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