Cine　imaging　serves　as　a　vital　approach　for　non-invasive　assessment　of　cardiac　functional　parameters.　The　imaging　process　of　Cine　cardiac　MRI　is　inherently　slow,　necessitating　the　acquisition　of　data　at　multiple　time　points　within　each　cardiac　cycle　to　ensure　adequate　temporal　resolution　and　motion　information.　Over　prolonged　data　acquisition　and　during　motion,　Cine　images　can　exhibit　image　degradation,　leading　to　the　occurrence　of　artifacts.　Conventional　image　reconstruction　methods　often　require　expert　knowledge　for　feature　selection,　which　may　result　in　information　loss　and　suboptimal　outcomes.　In　this　paper,　we　employ　a　data-driven　deep　learning　approach　to　address　this　issue.　This　approach　utilizes　supervised　learning　to　compare　data　with　different　acceleration　factors　to　full-sampled　spatial　domain　data,　training　a　context-aware　network　to　reconstruct　images　with　artifacts.　In　our　model　training　strategy,　we　employ　an　adversarial　approach　to　make　the　reconstructed　images　closer　to　ground　truth.　We　incorporate　loss　functions　based　on　adversarial　principles　and　introduce　image　quality　assessment　as　a　constraint.　Our　context-aware　model　efficiently　accomplishes　artifact　removal　and　image　reconstruction　tasks.