The　topic　of　super-resolution　image　reconstruction　has　recently　received　considerable　attention　within　the　research　area　Super-resolution　image　reconstruction　methods　attempt　to　create　a　single　high-resolution　image　from　a　number　of　low-resolution　images　(or　a　video　sequence).　The　Bayesian　maximum　a　posteriori　(AMP)　estimation　method　is　widely　used　for　super-resolution　image　reconstruction.　So　far　existing　super-resolution　reconstruction　methods　are　all　confronted　with　the　problem　of　slow　convergence　and　expensive　computation.　To　satisfy　the　requirement　of　real-time　application,　we　propose　a　high-efficiency　super-resolution　reconstruction　algorithm　that　solves　two　key　bottlenecks　in　the　multi-frame　MAP　framework.　The　first　breakthrough　is　to　select　the　Armijo　rule　to　identify　the　step　length　instead　of　the　exact　line　search.　The　second　one　is　to　approximately　compute　the　gradient　of　the　MAP　objective　function　using　analytic　representation　instead　of　numerical　calculation.　Experimental　results　show　that　the　proposed　algorithm　is　applicable　to　real-time　super-resolution　reconstruction　of　low-resolution　image　sequences.