Images　captured　in　a　real　scene　usually　suffer　from　complex　non-uniform　degradation,　which　includes　both　global　and　local　blurs.　It　is　difficult　to　handle　the　complex　blur　variances　by　a　unified　processing　model.　We　propose　a　global-local　blur　disentangling　network,　which　can　effectively　extract　global　and　local　blur　features　via　two　branches.　A　phased　training　scheme　is　designed　to　disentangle　the　global　and　local　blur　features,　that　is　the　branches　are　trained　with　task-specific　datasets,　respectively.　A　branch　attention　mechanism　is　introduced　to　dynamically　fuse　global　and　local　features.　Complex　blurry　images　are　used　to　train　the　attention　module　and　the　reconstruction　module.　The　visualized　feature　maps　of　different　branches　indicated　that　our　dual-branch　network　can　decouple　the　global　and　local　blur　features　efficiently.　Experimental　results　show　that　the　proposed　dual-branch　blur　disentangling　network　can　improve　both　the　subjective　and　objective　deblurring　effects　for　real　captured　images.