Open AccessUltrasound 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.