关键词: 超分辨率, 深度学习, 医学图像, 残差网络

Abstract: Improving the sharpness of medical images is of great significance for doctors to quickly diagnose and analyze the disease. A medical image super-resolution reconstruction algorithm based on residual network is proposed to fully improve the texture details of medical images. Firstly, this paper selects the appropriate data set, uses the very deep convolution neural network, cascades several smaller filters, extracts the information from the image adequately. Secondly, the residual learning method and the Adam optimization method are used to accelerate the convergence of the deep network model. Finally, training sets of different magnifications are combined into a hybrid data set for training, which improves performance while greatly reducing the number of parameters and training time. The experimental results show that the PSNR, SSIM and FSIM of the proposed algorithm are higher than the existing algorithms, the reconstructed image has more abundant details and more complete edges.

Key words: super-resolution, deep learning, medical image, residual network