Ultrasound Image Super-Resolution with Two-Stage Zero-Shot CycleGAN | Zendy

Jianrui Ding | Zendy; Shili Zhao | Zendy; Fenghe Tang | Zendy; Chunping Ning | Zendy

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Ultrasound Image Super-Resolution with Two-Stage Zero-Shot CycleGAN

Author(s) -

Jianrui Ding,

Shili Zhao,

Fenghe Tang,

Chunping Ning

Publication year - 2021

Publication title -

journal of physics conference series

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.21

H-Index - 85

eISSN - 1742-6596

pISSN - 1742-6588

DOI - 10.1088/1742-6596/2031/1/012015

Subject(s) - artificial intelligence , computer science , image (mathematics) , shot (pellet) , resolution (logic) , contrast (vision) , pattern recognition (psychology) , zero (linguistics) , computer vision , linguistics , chemistry , philosophy , organic chemistry

Medical ultrasound imaging is widely used in clinical diagnosis because of its non-invasive, convenient and quick characteristics. However, due to its low image contrast, multiple artifacts, noise and lack of paired high-resolution and low-resolution image data sets, the task of super-resolution reconstruction of medical ultrasound images is more challenging. In this paper, the Two-Stage GAN network model was adjusted by CycleGAN generation and unsupervised learning methods, and the Two-Stage ZSSR (“Zero-Shot” Super-Resolution) CycleGAN network was proposed. The objective evaluation indexes PSNR and SSIM were raised to 40.8079 and 0.9953. The visual effect was also significantly improved.

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