A　multi-local　kernels＇　fusion　algorithm　is　proposed　to　solve　the　problem　of　high　complexity　of　dark　channel　priors.　In　this　algorithm,　the　local　kernels　are　computed　and　solved　in　parallel,　and　then　merged　into　a　global　kernel　(point　spread　function)　by　using　the　shape　similarity　of　local　kernels.　For　the　noise　that　has　appeared　on　the　initially　merged　global　kernel,　the　relevance　adjustment　is　introduced　using　the　neighboring　information　to　further　improve　the　fusion　effect.　Experiments　show　that　the　proposed　algorithm　can　effectively　improve　the　speed　of　image　deblurring　while　guaranteeing　the　deblurring　effect.　It　also　has　better　effect　on　the　local　detail　restoration　of　some　real　blurred　images,　and　can　handle　large-size　blurred　images　well.　©　2018,　Science　Press.　All　right　reserved.