In　this　work,　a　multiple　source　separation　method　with　joint　sparse　and　non-sparse　components　recovery　is　proposed　by　using　dual　similarity　determination.　Specifically,　a　dual　similarity　coefficient　is　designed　based　on　normalized　cross-correlation　and　Jaccard　coefficients,　and　its　reasonability　is　validated　via　a　statistical　analysis　on　a　quantitative　effective　measure.　Thereafter,　by　regarding　the　sparse　components　as　a　guide,　the　non-sparse　components　are　recovered　using　the　dual　similarity　coefficient.　Eventually,　a　separated　signal　is　obtained　by　a　synthesis　of　the　sparse　and　non-sparse　components.　Experimental　results　demonstrate　the　separation　quality　of　the　proposed　method　outperforms　some　existing　BSS　methods　including　sparse　components　separation　based　methods,　independent　components　analysis　based　methods　and　soft　threshold　based　methods.