In　recent　years,　convolutional　neural　networks　have　excelled　in　image　Moiré　pattern　removal,　yet　their　high　memory　consumption　poses　challenges　for　resource-constrained　devices.　To　address　this,　we　propose　the　lightweight　multi-scale　network　(LMSNet).　Designing　lightweight　multi-scale　feature　extraction　blocks　and　efficient　adaptive　channel　fusion　modules,　we　extend　the　receptive　field　of　feature　extraction　and　introduce　lightweight　convolutional　decomposition.　LMSNet　achieves　a　balance　between　parameter　numbers　and　reconstruction　performance.　Extensive　experiments　demonstrate　that　our　LMSNet,　with　0.77　million　parameters,　achieves　Moiré　pattern　removal　performance　comparable　to　full　high　definition　demoiréing　network　(FHDe2Net)　with　13.57　million　parameters.　©　2024　SPIE　and　IS&T.