In　recent　years,　convolutional　neural　networks　have　excelled　in　image　Moir　&　eacute;　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　Moir　&　eacute;　pattern　removal　performance　comparable　to　full　high　definition　demoir　&　eacute;ing　network　(FHDe(2)Net)　with　13.57　million　parameters.