## 1 Introduction  
MRI has been a prominent brain abnormality diagnosis tool in the recent decade. HR medical imaging shows detail. Thus, high-quality visualization demand is expanding significantly. MRI scanner limits and transmission bandwidth difficulties may make it difficult to obtain brain magnetic resonance (MR) images with optimal resolution for application. Recently, picture signal processing research has focused on super-resolution picture reconstruction. Addressing the problem leads to an enhancement of this particular area. As stated by the source referenced as [1], the ability to achieve high-resolution imaging is impeded by hardware and physics limitations. Consequently, this results in extended scan durations, restricted spatial coverage, and diminished signal-to-noise ratio. According to [2], the unsettled nature of SR makes resolution challenging. After resolution loss, unlimited high-quality photographs may yield the same low-resolution image. Restoring texture and structure is hard. SR is a convex optimization problem that aims to resolve HR issues while maintaining regularization [3]. Regularization terms require initial picture distribution knowledge, usually empirical conjectures. Piecewise stability is assumed by popular limitations like complete variation. This method may struggle with photographs of intricate local architecture and features. Learning-oriented methods require less prior information. Deep learning algorithms have the potential to effectively replicate the intricate connections between low-resolution and high-resolution images, hence enhancing the Single-Image Super-Resolution (SISR) process for intricate images, even in demanding circumstances. Super-Resolution SRCNNs and their speedier variants can provide high-quality Single-Image Super-Resolution results for 2D natural photographs utilizing structured CNNs. Several studies reveal this [4; 5]. Many decades ago, classic algorithms were divided into patch, edge, sparse coding, prediction, and statistics. These methods are computationally cheaper than deep learning. Also restricted is information recovery. Deep learning has made convolutional neural networks popular, enhancing SR. Early deep-learning systems cannot super-resolve medical images. Medical imaging uses 3D volumes. Previous CNNs only worked per slice, rejecting input from neighboring third-dimensional structures. Three-dimensional models include more parameters than two-dimensional models, requiring more memory and computing resources and reducing their flexibility. A study [6] found that MSE and PSNR are unreliable measures of picture visual accuracy. Sharpness and integrity decrease with MSE optimization alone. The utilization of 3D Multi-Level Densely Connected Super-Resolution Networks (mDCSRNs) has the potential to provide assistance in this matter. According to [7], a strongly coupled network gives the mDCSRN a low weight. The model's size and speed increase with intensity difference optimization without sacrificing performance. System performance improves with GAN training. Images with more clarity and authenticity show this. According to [8], super-resolution (SR) technology enhances computer vision tasks like semantic segmentation. HR data collection using super-resolution (SR) technology has several uses. The choice of the Video Super-Resolution (VSR) algorithm is critical. Partitioning the VSR technique into multi-frame SR subtasks led to flicker artifacts and computationally expensive procedures [9; 10]. The above methods ignore people's perceptions, resulting in poor super-resolution reconstruction. In SR, GANs were introduced. Computer vision applications like super-resolution used GANs. The Generative Adversarial Network for image super-resolution (SR) SRGAN recovers high-quality textures in low-resolution (LR) images using adversarial and perceptual loss. This network revives textures and high-frequency components. But it is restricted. Noise and shifting are caused by GAN.