Wu Yangyang, Yang Feng, Huang Jing, Liu Yaqin
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2019 Jan 30;39(1):82-87. doi: 10.12122/j.issn.1673-4254.2019.01.13.
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.
低分辨率超声图像视觉效果较差。在此，我们提出一种基于超分辨率重建结合生成对抗网络生成更清晰血管内超声图像的方法。我们使用生成对抗网络通过生成器生成图像，并通过判别器估计图像的真实性。具体而言，低分辨率图像通过亚像素卷积层特征通道生成相同大小的特征图，然后将每个特征图中的对应像素重新排列成×子块，这些子块对应于高分辨率图像中的子块；放大后，生成具有倍分辨率的图像。生成对抗网络通过不断优化可获得更清晰的图像。我们将该方法（SRGAN）与其他方法（包括双立方插值、超分辨率卷积网络（SRCNN）和高效亚像素卷积网络（ESPCN））进行了比较，结果表明所提方法在峰值信噪比（PSNR）上显著提高了2.369 dB，在结构相似性指数上提高了1.79%，从而增强了血管内超声图像的诊断视觉效果。
