Convolutional　neural　networks　(CNN)　have　shown　its　excellent　performance　in　computer　vision　fields.　Recently,　they　are　successfully　applied　to　image　restoration.　This　paper　proposes　a　joint　blur　kernel　estimation　and　CNN　method　for　blind　image　restoration.　The　blur　kernel　estimation　is　based　on　both　blur　support　parameter　estimation　and　blur　type　identification.　An　automatic　feature　line　detection　algorithm　is　presented　for　blur　support　parameter　estimation　and　a　dictionary　learning　algorithm　is　presented　for　the　blur　type　identification.　Once　the　blur　kernel　estimate　is　obtained,　we　use　an　effective　CNN　for　iterative　non-blind　deconvolution,　which　is　able　to　automatically　learn　image　priors.　Compared　with　current　blind　image　restoration　methods,　the　proposed　joint　method　can　obtain　restored　images　under　three　types　of　unknown　blur　kernels.　The　experimental　result　shows　that　the　proposed　blur　kernel　estimation　algorithm　can　provide　high　accuracy　results.　Furthermore,　the　proposed　joint　blur　kernel　estimation　and　CNN　algorithm　is　superior　to　conventional　blind　image　restoration　algorithms　in　terms　of　restoration　quality　and　computation　time.　(C)　2019　Elsevier　B.V.　All　rights　reserved.