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
em Zh
| WPRIM
| ID: wpr-772117
Biblioteca responsável:
WPRO
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.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Vasos Sanguíneos
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Processamento de Imagem Assistida por Computador
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Diagnóstico por Imagem
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Aumento da Imagem
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Endossonografia
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Razão Sinal-Ruído
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Métodos
Tipo de estudo:
Diagnostic_studies
Idioma:
Zh
Revista:
Journal of Southern Medical University
Ano de publicação:
2019
Tipo de documento:
Article