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.