基于生成对抗网络的超声图像超分辨率重建

针对医学超声图像的分辨率低而导致视觉效果差的问题，使用基于神经网络的图像超分辨率(SR)重建方法提升医学超声图像的分辨率。采用针对自然图像超分辨率重建的生成对抗网络(SRGAN)作为基本方法，通过减少2个输入通道和删除1个残差块对该网络的结构进行更改，并且改进网络损失函数，新增模糊处理数据集，使该网络适应医学超声图像所具备的灰度图像、散斑纹理单一等特点，从而重建出放大4倍的边缘清晰没有伪影的医学超声图像。将改进SRGAN与原始SRGAN的结果相比，峰值信噪比(PSNR)和结构相似性(SSIM)分别有1.792 dB和3.907%的提升；与传统双立方插值的结果相比，PSNR和SSIM分别有2.172 dB和8.732%的提升。

Super-resolution reconstruction of ultrasound images based on a generative adversarial network

To tackle with the problem of poor visual effects caused by low-resolution of medical ultrasound images, a neural network based image Super-Resolution(SR) reconstruction approach is employed to improve the resolution of medical ultrasound images. Based on the Generative Adversarial Network for Super-Resolution(SRGAN), the structure of the network is changed by reducing two input channels and deleting a residual block. A fuzzy dataset is added and the loss function of the network is improved according to the characteristics of medical ultrasound images, such as gray-scale image and single speckle texture, so that the network is adapted to reconstruct the clear edges with 4 times magnification of medical ultrasound images without artifacts. Comparing the results of the improved SRGAN with the original SRGAN, the Peak Signal-to-Noise Ratio(PSNR) and Structural SIMilarity(SSIM) are increased by 1.792 dB and 3.907% respectively; compared with Bicubic interpolation, the PSNR and SSIM are increased by 2.172% dB and 8.732% respectively.