基于深度残差生成对抗网络的医学影像超分辨率算法

针对医学影像超分辨率重建过程中细节丢失导致的模糊问题，提出了一种基于深度残差生成对抗网络（GAN）的医学影像超分辨率算法。首先，算法包括生成器网络和判别器网络，生成器网络生成高分辨率图像，判别器网络辨别图像真伪。然后，通过设计生成器网络的上采样采用缩放卷积来削弱棋盘效应，并去掉标准残差块中的批量规范化层以优化网络；进一步增加判别器网络中特征图数量以加深网络等方面提高网络性能。最后，用生成损失和判别损失来不断优化网络，指导生成高质量的图像。实验结果表明，对比双线性内插、最近邻插值、双三次插值法、基于深度递归神经网络、基于生成对抗网络的超分辨率方法（SRGAN），所提算法重建出了纹理更丰富、视觉更逼真的图像。相比SRGAN方法，所提算法在峰值信噪比（PSNR）和结构相似度（SSIM）上有0.21 dB和0.32%的提升。所提算法为医学影像超分辨率的理论研究提供了深度残差生成对抗网络的方法，在其实际应用中可靠、有效。

Medical image super-resolution algorithm based on deep residual generative adversarial network

Aiming at the ambiguity caused by the loss of details in the super-resolution reconstruction of medical images, a medical image super-resolution algorithm based on deep residual Generative Adversarial Network (GAN) was proposed. Firstly, a generative network and a discriminative network were designed in the method. High resolution images were generated by the generative network and the authenticities of the images were identified by the discriminative network. Secondly, a resize-convolution was used to eliminate checkerboard artifacts in the upsampling layer of the designed generative network and the batch-normalization layer of the standard residual block was removed to optimize the network. Also, the number of feature maps was further increased in the discriminative network and the network was deepened to improve the network performance. Finally, the network was continuously optimized according to the generative loss and the discriminative loss to guide the generation of high-quality images. The experimental results show that compared with bilinear interpolation, nearest-neighbor interpolation, bicubic interpolation, deeply-recursive convolutional network for image super-resolution and Super-Resolution using a Generative Adversarial Network (SRGAN), the improved algorithm can reconstruct the images with richer texture and more realistic vision. Compared with SRGAN, the proposed algorithm has an increase of 0.21 dB and 0.32% in Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM). It provides a deep residual generative adversarial network method for the theoretical research of medical image super-resolution, which is reliable and effective in practical applications.