# Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images
## 1 Introduction
ESRGAN [19]: Compared with SRGAN, ESRGAN increases the perceptual loss, and uses the Residual-in-Residual Dense Block (RDDB) module to replace the original Residual Block module, so that ESRGAN can obtain more realistic and natural textures. SRFBN [14]: SRFBN designed a feedback module, using the return mechanism to improve the effect of super-resolution. The advantage of a returnback is that no additional parameters are added, and multiple returnbacks are equivalent to deepening the network, refining the generated image. Although other networks have adopted similar return structures, these networks are unable to make the front layer obtain useful information from the back layer. ESRFBN [20]: For ESRFBN, the number of feature graphs and group convolutions is increased on the basis of the SRFBN network. The number of feature graphs is changed from 32 to 64, and the number of group convolution is also increased from 3 to 6. However, these deep learning networks are mainly for natural image super-resolution tasks, not medical images or MR images. In this regard, medical image researchers have also paid attention to the progress of deep super-resolution, and transferred deep learning technology to MR images, and a number of deep-learning-based MR images super-resolution methods have emerged. For example, a Progressive Sub-band Residual learning Super-Resolution Network (PSR-SRN) is proposed in [21], which contains two parallel progressive learning streams. One stream passes through the sub-band residual learning unit to detect the missing high-frequency residuals, and the other stream focuses on the reconstruction of refined MR images. These two learning streams complement each other and learn the complex mapping between high- and low-resolution MR images. In [22], researchers proposed a new hybrid network, which improves the quality of MR images by increasing the width of the network. The hybrid block combines multi-path structure and the variation dense block, which can extract rich features from low-resolution images. In the previous work [23], the researcher combined the meta-learning technology with the Generative adversarial networks (GAN) to achieve super-resolution of MR images of any scale and high fidelity, and the meta-learning technology was used in the work [24]. Generative adversarial networks (GAN) are a deep learning model. It is one of the most promising methods of unsupervised learning on complex distribution in recent years. The model produces fairly good output through mutual game learning of two modules in the framework: generative model and discriminant model. GAN is most commonly used in image generation, such as super-resolution tasks, semantic segmentation and so on. The Super Resolution scale is the scaling factor. The scaling operation will adjust the size of the image according to the given scaling factor. In this paper, data sets with two super-resolution scales of \(\times 2\) and \(\times 4\) are generated in this paper. However, the current deep learning technology still has three limitations when applying to MR images' super-resolution. (1) The characteristics of MR images' sequence are not fully considered. The imaging method of MR images is different from natural images. In the process of MR images, a series of images with similar structures can be obtained. Therefore, compared with natural images, MR sequence images can provide richer structural information and make the network more robust to noise. (2) At present, most deep networks adopt L1 or L2 loss functions, which are all based on a pixel-by-pixel comparison without considering human visual perception. At the same time, they are easy to fall into the local optimal solution, and it is difficult to obtain the optimal effect [20]. (3) The capability of feature representation of deep learning networks gradually increases with the deepening of the network.