生成对抗网络的血管内超声图像超分辨率重建

针对超声图像分辨率低导致视觉效果差的问题，本文以超分辨率重建为基础，结合生成对抗网络的方法，生成相对原图更加清晰的血管内超声图像，用于辅助医生诊断与治疗。本方法应用生成对抗网络，生成器生成图像，判别器判断图像真伪。其过程：低分辨率图像经过亚像素卷积层r2个特征通道，产生尺寸大小相同的r2个特征图，对每个特征图中相对应的同一像素重新排列成一个r×r的子块，其对应高分辨率图像中的某一个子块，经过放大，产生r2倍的高分辨率图像。生成对抗网络经过不断优化，获得更优质清晰的图像。将本方法（SRGAN）得出的结果与双立方插值（Bicubic）、超分辨率卷积网络（SRCNN）和亚像素卷积网络（ESPCN）等方法比较，其峰值信噪比（PSNR）和结构相似性（SSIM）分别提高2.369dB和1.79%。因此，我们得知：结合生成对抗网络的图像超分辨率重建能获得很好的血管内超声图像诊断视觉效果。

Super-resolution construction of intravascular ultrasound images using generativeadversarial networks

The low-resolution ultrasound images have poor visual effects. Herein we propose a method for generating clearerintravascular ultrasound images based on super-resolution reconstruction combined with generative adversarial networks. Weused the generative adversarial networks to generate the images by a generator and to estimate the authenticity of the imagesby a discriminator. Specifically, the low-resolution image was passed through the sub-pixel convolution layer r2-featurechannels to generate r2-feature maps in the same size, followed by realignment of the corresponding pixels in each feature mapinto r × r sub-blocks, which corresponded to the sub-block in a high-resolution image; after amplification, an image with ar2-time resolution was generated. The generative adversarial networks can obtain a clearer image through continuousoptimization. We compared the method (SRGAN) with other methods including Bicubic, super-resolution convolutionalnetwork (SRCNN) and efficient sub-pixel convolutional network (ESPCN), and the proposed method resulted in obviousimprovements in the peak signal-to-noise ratio (PSNR) by 2.369 dB and in structural similarity index by 1.79% to enhance thediagnostic visual effects of intravascular ultrasound images.