As　brain　investigation　progresses,　the　need　has　become　urgent　from　acquiring　the　higher　resolution　neuroimaging　data　to　give　a　more　detailed　interpretation.　In　particular,　the　technological　development　and　innovation　of　the　Magnetic　Resonance　Imaging　(MRI)　machine,　through　increasing　the　magnetic　field　from　low　(such　as　3T)　to　high　(such　as　7T),　has　revealed　significant　advantages　regarding　the　image　quality　enhancement,　etc.　Currently,　due　to　the　limitations　of　hardware,　physics　and　physiology,　the　large-scale　acquisition　of　the　high-resolution　MRI　neuroimages　is　still　running　on　the　road.　Hence,　enhancing　the　quality　of　the　low-field　MRI　data　is　critical　by　using　the　advanced　artificial　intelligence　technology.　In　this　study,　we　propose　a　novel　image　enhancement　framework,　namely　SR-MRI,　trying　to　improve　the　quality　of　the　low-resolution　neuroimage:　(1)　combining　with　the　Real-ESRGAN　deep　learning　model;　(2)　bridging　the　3T-MRI　and　the　7T-MRI　within　the　same　analysis　scale;　and　(3)　systematically　comparing　multiple　evaluation　indicators,　including　Brenner,　SMD,　SMD2,　Variance,　Vollath,　Entropy,　and　NIQE.　The　experimental　results　suggest　that　the　edge,　fineness　and　texture　features　of　the　low-resolution　neuroimages　are　restored　to　a　great　extent　from　the　SR-MRI　framework.　In　addition,　the　evaluation　results　of　multiple　indicators　show　that　the　processed　3T-MRI　can　achieve　the　similar　effect　from　the　7T-MRI　machine.　©　2022,　Springer　Nature　Switzerland　AG.