Drone　monitoring　plays　an　irreplaceable　and　significant　role　in　forest　firefighting　due　to　its　characteristics　of　wide-range　observation　and　real-time　messaging.　However,　aerial　images　are　often　susceptible　to　different　degradation　problems　before　performing　high-level　visual　tasks　including　but　not　limited　to　smoke　detection,　fire　classification,　and　regional　localization.　Recently,　the　majority　of　image　enhancement　methods　are　centered　around　particular　types　of　degradation,　necessitating　the　memory　unit　to　accommodate　different　models　for　distinct　scenarios　in　practical　applications.　Furthermore,　such　a　paradigm　requires　wasted　computational　and　storage　resources　to　determine　the　type　of　degradation,　making　it　difficult　to　meet　the　real-time　and　lightweight　requirements　of　real-world　scenarios.　In　this　paper,　we　propose　an　All-in-one　Image　Enhancement　Network　(AIENet)　that　can　restore　various　degraded　images　in　one　network.　Specifically,　we　design　a　new　multi-scale　receptive　field　image　enhancement　block,　which　can　better　reconstruct　high-resolution　details　of　target　regions　of　different　sizes.　In　particular,　this　plug-and-play　module　enables　it　to　be　embedded　in　any　learning-based　model.　And　it　has　better　flexibility　and　generalization　in　practical　applications.　This　paper　takes　three　challenging　image　enhancement　tasks　encountered　in　drone　monitoring　as　examples,　whereby　we　conduct　task-specific　and　all-in-one　image　enhancement　experiments　on　a　synthetic　forest　dataset.　The　results　show　that　the　proposed　AIENet　outperforms　the　state-of-the-art　image　enhancement　algorithms　quantitatively　and　qualitatively.　Furthermore,　extra　experiments　on　high-level　vision　detection　also　show　the　promising　performance　of　our　method　compared　with　some　recent　baselines.