The　current　strive　toward　efficient　intelligent　visual　systems　suffers　from　challenges　in　the　task　of　low-light　image　enhancement.　To　improve　image　perception,　the　low-light　scenes　in　different　illumination　conditions　must　be　properly　focused.　However,　typical　CNN-based　methods　use　the　same　set　of　parameters　for　all　images,　which　limits　the　capability　for　handling　complex　scenes.　Meanwhile,　the　existing　deep　models　integrate　the　low-level　and　high-level　features　by　simply　adding　or　concatenating　operations,　lacking　unique　designs　for　the　low-light　image　enhancement　task.　To　address　the　above　challenges,　we　propose　a　zero-referenced　adaptive　filter　network　(ZAFN)　for　low-light　image　enhancement.　Specifically,　the　adaptive　filters　are　generated　by　the　integration　of　high-level　contents　from　multiple　partial　scenes.　The　iterative　enlightening　process　is　then　conducted　using　the　low-level　features　that　are　dynamically　modulated　with　the　adaptive　filters.　To　alleviate　the　requirement　of　paired　training　data　and　enable　zero-referenced　learning,　we　propose　a　color　enhancement　loss,　a　global　consistency　loss,　and　a　self-regularized　denoising　loss　for　high-quality　results.　Our　ZAFN　model,　which　has　a　light　model　size　and　low　computational　cost,　outperforms　other　state-of-the-art　zero-referenced　methods　on　four　popular　datasets.