Lately, Magnetic Resonance scans have struggled with their own inherent limitations, such as spatial resolution as well as long examination times. A novel, rapid compressively-sensed magnetic resonance high-resolution image resolution algorithm is presented in this research paper. This technique addresses these two key issues by employing a highly-sparse sampling scheme and super-resolution reconstruction (SRR) method. Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a deformable human body motion analysis. Clinical trials as well as a phantom-based study have been conducted. It has been proven that the proposed algorithm can enhance image spatial resolution and reduce motion artefacts and scan times.