# Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images
## 2 Algorithm Architecture  
Super-resolution methods based on deep learning have been successively applied to MR images. However, these methods simply use deep learning techniques to process MR image super-resolution tasks without fully considering the difference between natural images and medical images. Deep learning technology still has limitations in MR images' super-resolution. In this paper, corresponding optimization methods are proposed for the three limitations of the application of current deep super-resolution networks in MR images.  
Aiming at the problem that the characteristics of MR images' sequence are not fully considered, this chapter proposes a multi-slice input strategy to provide more structural information for the network. The MR images are a sequence of images. Its adjacent slices have a similar structure, which can provide two-point-five dimension (2.5D) information for the network. It can obtain better results than two-dimension (2D), and has fewer parameters and higher efficiency than three-dimension (3D) convolution [25].  
Aiming at the problem that the L1 or L2 loss function is based on a pixel-by-pixel comparison without considering human visual perception, where the L1 loss function is the Least Absolute value Deviation (LAD) loss function, and the L2 norm loss function is the Least Square Error (LSE) loss function. We propose a method of multi-loss function cascade optimization based on the analysis of the commonly used loss functions. By combining the characteristics of the L1 loss function to retain color and brightness and the characteristics of the MS-SSIM loss function to retain the contrast of the high-frequency region better, the depth model has better characterization performance [26].  
Aiming at the problem that it is difficult for large deep learning networks to balance model complexity and training difficulty, this paper proposes a method based on heterogeneous network fusion [27]. Heterogeneous network fusion is the use of common technologies to realize the interconnection of different structures of super-resolution networks. In addition, the different structures have the different ability of MR super-resolution. Heterogeneous network fusion is to increase the super-resolution performance through applying enough of the advantages from these heterogeneous networks. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, or two independent networks are cascaded. This method essentially broadens the width and depth of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features, so as to obtain a better MR images' super-resolution effect.  
In order to solve the three limitations of deep super-resolution networks on MR images simultaneously, this paper proposes a multi-optimized ESRFBN super-resolution network algorithm for MR images based on the optimization methods of the previous three paragraphs. The algorithm architecture is shown in Figure 1. The three optimization methods are applied to the ESRFBN network at the same time, so that the multi-optimized ESRFBN network can get a better MR images' super-resolution effect.