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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Ultrasound Image Super-Resolution with Two-Stage Zero-Shot CycleGAN
Semantic Scholar · Medicine · 2021
Abstract
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.