Research on super resolution reconstruction of medical images based on recurrent generative adversarial networks
Jiashu Wang, Duanmu Chunjiang · 2024
In today's hospitals, doctors often need to use medical equipment to find the cause of the disease, and the clarity of medical images often affects the doctor's judgment of the patient's condition. Having high-resolution medical images can help doctors better treat patients. But nowadays, most super-resolution models cannot effectively restore medical images, resulting in poor image restoration results. This article follows the idea of cyclic generative networks and trains them using Wasserstein distance to solve the task of image reconstruction. The model consists of an image reconstruction network, an image degradation network, and two discriminators. In the generative network, we implement cyclic consistency based on Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR output images. This article experimented and validated the performance on the FastMRI dataset, and compared with existing mainstream methods, the results showed that it was superior to current mainstream methods in medical image super-resolution reconstruction.