Unmanned　aerial　vehicle　(UAV)　based　visual　systems　suffer　from　poor　perception　at　nighttime.　There　are　three　challenges　for　enlightening　nighttime　vision　for　UAVs:　Firstly,　the　UAV　nighttime　images　differ　from　underexposed　images　in　the　statistical　characteristic,　limiting　the　performance　of　general　low-light　image　enhancement　(LLIE)　methods.　Secondly,　when　enlightening　nighttime　images,　the　artifacts　tend　to　be　amplified,　distracting　the　visual　perception　of　UAVs.　Thirdly,　due　to　the　inherent　scarcity　of　paired　data　in　the　real　world,　it　is　difficult　for　UAV　nighttime　vision　to　benefit　from　supervised　learning.　To　meet　these　challenges,　we　propose　a　zero-referenced　enlightening　and　restoration　network　(ZERNet)　for　improving　the　perception　of　UAV　vision　at　nighttime.　Specifically,　by　estimating　the　nighttime　enlightening　map　(NE-map),　a　pixel-to-pixel　transformation　is　then　conducted　to　enlighten　the　dark　pixels　while　suppressing　overbright　pixels.　Furthermore,　we　propose　the　self-regularized　restoration　to　preserve　the　semantic　contents　and　restrict　the　artifacts　in　the　final　result.　Finally,　our　method　is　derived　from　zero-referenced　learning,　which　is　free　from　paired　training　data.　Comprehensive　experiments　show　that　the　proposed　ZERNet　effectively　improves　the　nighttime　visual　perception　of　UAVs　on　quantitative　metrics,　qualitative　comparisons,　and　application-based　analysis.　IEEE