The　defocus　deblurring　raised　from　the　finite　aperture　size　and　exposure　time　is　an　essential　problem　in　the　shooting　process,　which　seriously　affects　the　quality　of　the　images.　However,　studies　based　on　defocus　deblurring　in　monocular　images　yielded　good　results,　while　those　on　binocular　images　are　rare.　The　current　methods　directly　merge　the　left　and　right　views　regardless　of　their　unique　features.　Objects　within　the　camera＇s　DoF　will　not　have　a　difference　in　phase,　while　light　rays　from　outside　the　DoF　will　have　a　relative　shift　that　is　directly　correlated　with　the　amount　of　defocus　blur.　In　this　paper,　we　firstly　proposed　an　enhanced　multi-stage　network　for　defocus　deblurring　using　dual-pixel　Images.　Taking　into　account　the　parallax　between　the　left　and　right　views,　the　first　two　stages　learn　the　information　of　them,　respectively,　and　correct　the　deviation　of　the　images　under　the　supervision　of　the　ground　truth.　The　third　stage　consists　of　EERG　and　ERGS.　It　merges　with　the　feature　map　of　the　previous　stage,　so　that　the　left　and　right　views　are　mutually　enhanced,　and　a　good　restored　image　is　obtained.　ERGS　uses　the　residual　block　as　the　basic　unit　to　restore　the　details　of　the　blurred　area　while　maintaining　the　clear.　Experimental　results　show　that　our　proposed　network　can　achieve　better　accuracy　than　state-of-the-art　approaches　on　the　public　DPD　dataset.　©　COPYRIGHT　SPIE.