Blind　super-resolution　image　reconstruction　is　to　obtain　a　high-resolution　image　from　a　sequence　of　low-resolution　images　which　are　degraded　by　unknown　blur,　noise,　and　down　sample.　Conventional　super-resolution　image　reconstruction　algorithms　assumed　that　the　blur　type　is　known,　thus　automatic　blur　identification　is　of　important　significance　in　blind　superresolution　image　reconstruction.　This　paper　proposed　a　novel　blur　type　identification　algorithm　for　blind　image　superresolution.　The　proposed　blur　type　identification　method　uses　a　dictionary　learning　to　identify　three　blur　kernels.　It　includes　the　logarithmic　normalized　feature　matrix,　the　structural　similarity　index,　and　the　best　structural　similarity　between　observed　images　and　dictionary　images.　Furthermore,　we　applied　the　proposed　blur　type　identification　method　to　blind　image　super-resolution.　The　experimental　result　shows　that　the　identification　accuracy　of　proposed　method　can　achieve　98%　above.　More　importantly,　the　proposed　blur　type　identification-based　algorithm　for　blind　image　super-resolution　can　enhance　the　performance　of　reconstruction　quality　according　to　visual　quality　and　evaluation　index.　©　2017　IEEE.