Despite　the　rapid　development　of　photography　equipment,　shooting　high-definition　RAW　images　in　extreme　low-light　environments　has　always　been　a　difficult　problem　to　solve.　Existing　methods　use　neural　networks　to　automatically　learn　the　mapping　from　extreme　low-light　noise　RAW　images　to　long-exposure　RGB　images　for　jointly　denoising　and　demosaicing　of　extreme　low-light　images,　but　the　performance　on　other　datasets　is　unpleasant.　In　order　to　address　this　problem,　we　present　a　separable　Unet++　(SUnet++)　network　structure　to　improve　the　generalization　ability　of　the　joint　denoising　and　demosaicing　method　for　extreme　low-light　images.　We　introduce　Unet++　to　adapt　the　model　to　other　datasets,　and　then　replace　the　conventional　convolutions　of　Unet++　with　M　sets　of　depthwise　separable　convolutions,　which　greatly　reduced　the　number　of　parameters　without　losing　performance.　Experimental　results　on　SID　and　ELD　dataset　demonstrate　our　proposed　SUnet++　outperform　the　state-of-the-arts　methods　in　term　of　subjective　and　objective　results,　which　further　validates　the　robust　generalization　of　our　proposed　method.　©　2022,　Springer　Nature　Switzerland　AG.