In　this　paper,　a　novel　L-2-norm　estimation　method　for　blind　data　fusion　under　noisy　environments　is　proposed　and　a　fast　learning　algorithm　is　developed　to　implement　the　proposed　estimation　method.　The　proposed　learning　algorithm　is　proved　to　be　globally　exponentially　convergent　to　an　optimal　fusion　weight　vector.　In　addition,　the　proposed　learning　algorithm　has　lower　computation　complexity　than　the　existing　cooperative　learning　algorithm　based　a　L-1-norm　estimation　method.　Compared　with　other　estimation　methods,　the　proposed　estimation　method　can　be　effectively　used　in　the　blind　image　fusion.　Application　examples　of　image　fusion　show　that　the　proposed　learning　algorithm　is　able　to　fast　obtain　more　accurate　solutions　than　several　conventional　algorithms.