Chest X-ray images super-resolution reconstruction via recursive neural network

To address the problems of insufficient detail extraction and long training time in the super-resolution reconstruction of chest X-ray images, a method of chest X-ray images super-resolution reconstruction using recursive neural network is proposed in this paper. Firstly, this paper designs a lightweight recursive network as the main branch, which solves the problem of training difficulty and time-consuming. Then, to overcome the lack of detail extraction in chest X-ray image, a detail complementary model is designed as another branch of the network to solve the problem of shallow information loss. Finally, the optimized activation function is used to reduce the loss of texture details and make the reconstructed image more complete and richer. When the scale factor is 2, the experimental results show that compared with other methods based on deep learning, such as the deep recursive neural network (DRCN), the details of chest X-ray images reconstructed by our method are more abundant. Specifically, the average value of PSNR and SSIM were improved by 0.17 dB and 0.0013 respectively. Moreover, the reconstruction speed of the images was increased by about 16% compared with DRCN.