Capturing　images　at　night　are　susceptible　to　inadequate　illumination　conditions　and　motion　blurring.　Given　the　typical　coupling　of　these　two　forms　of　degradation,　a　pioneer　work　takes　a　compact　approach　of　brightening　followed　by　deblurring.　However,　this　sequential　approach　may　compromise　informative　features　and　elevate　the　likelihood　of　generating　unintended　artifacts.　In　this　paper,　we　observe　that　the　co-existing　low　light　and　blurs　intuitively　impair　multiple　perceptions,　making　it　difficult　to　produce　visually　appealing　results.　To　meet　these　challenges,　we　propose　perceptual　decoupling　with　heterogeneous　auxiliary　tasks　(PDHAT)　for　joint　low-light　image　enhancement　and　deblurring.　Based　on　the　crucial　perceptual　properties　of　the　two　degradations,　we　construct　two　individual　auxiliary　tasks:　coarse　preview　prediction　(CPP)　and　high-frequency　reconstruction　(HFR),　so　that　the　perception　of　color,　brightness,　edges,　and　details　are　decoupled　into　heterogeneous　auxiliary　tasks　to　obtain　task-specific　representations　for　parallel　assisting　the　main　task:　joint　low-light　enhancement　and　deblurring　(LLE-Deblur).　Furthermore,　we　develop　dedicated　modules　to　build　the　network　blocks　in　each　branch　based　on　the　exclusive　properties　of　each　task.　Comprehensive　experiments　are　conducted　on　LOL-Blur　and　Real-LOL-Blur　datasets,　showing　that　our　method　outperforms　existing　methods　on　quantitative　metrics　and　qualitative　results.