Employing　specific　networks　to　address　different　types　of　degradation　often　proved　to　be　complex　and　time-consuming　in　practical　applications.　The　Bracket　Image　Restoration　and　Enhancement　(BIRE)　aimed　to　address　various　image　restoration　tasks　in　a　unified　manner　by　restoring　clear　single-frame　images　from　multiple-frame　shots,　including　denoising,　deblurring,　enhancing　high　dynamic　range　(HDR),　and　achieving　super-resolution　under　various　degradation　conditions.　In　this　paper,　we　propose　LGSTANet,　an　efficient　aggregation　restoration　network　for　BIRE.　Specifically,　inspired　by　video　restoration　methods,　we　adopt　an　efficient　architecture　comprising　alignment,　aggregation,　and　reconstruction.　Additionally,　we　introduce　a　Learnable　Global　Spatio-Temporal　Adaptive　(LGSTA)　aggregation　module　to　effectively　aggregate　inter-frame　complementary　information.　Furthermore,　we　propose　an　adaptive　restoration　modulator　to　address　specific　degradation　disturbances　of　various　types,　thereby　achieving　high-quality　restoration　outcomes.　Extensive　experiments　demonstrate　the　effectiveness　of　our　method.　LGSTANet　outperforms　other　state-of-the-art　methods　in　Bracket　Image　Restoration　and　Enhancement　and　achieves　competitive　results　in　the　NTIRE2024　BIRE　challenge.