Blind　image　deblurring　is　the　process　of　removing　blurring　artifacts　from　the　observation　when　the　blur　kernel　is　unknown,　which　is　a　seriously　ill-posed　problem.　It　is　indispensable　to　impose　prior　information　constraints　on　the　feasible　set.　Inspired　by　the　SelfDeblur,　in　this　paper　we　propose　a　deep　prior-based　blind　image　deblurring　method,　which　uses　the　deep　network　and　the　regularized　optimization　model　to　jointly　optimize　and　alternately　update　the　latent　image　and　the　blur　kernel.　Conditioned　by　the　loss　of　the　sum　of　RGB　three-channel　errors　with　the　presence　of　blur　kernel,　the　latent　image　is　estimated　using　the　deep　convolutional　neural　network　DIP-Net　implicitly　involving　the　smoothness　regularizer　of　the　image.　The　blur　kernel　estimation　subproblem　admits　the　global　optimal　solution,　which　is　different　from　the　SelfDeblur　that　applies　the　fully　connected　network　and　takes　the　gradient　descent　step　to　update　the　blur　kernel.　Our　method　uses　the　structure　of　the　deep　network　to　regularize　the　latent　image.　Unlike　the　supervised　image　deblurring　method,　it　requires　no　ground　truth　of　the　latent　image　or　the　blur　kernel.　Unlike　the　traditional　model,　it　requires　no　progressive　transmission　from　coarse　to　fine　through　the　multi-level　pyramid.　Experimental　results　on　simulated　and　real　blur　images　show　that　the　proposed　method　achieves　a　fast　and　accurate　estimation　of　both　the　blur　kernel　and　the　latent　image　with　efficient　noise　suppression.　©　2023　Chinese　Institute　of　Electronics.　All　rights　reserved.