Bandpass　filter-based　multispectral　(MS)　imaging　systems　have　difficulty　achieving　high-quality　MS　imaging　results　while　capturing　high　spatial　resolution　MS　data　cubes.　This　paper　proposes　a　notch　filter-based　low-cost　multicamera　MS　imaging　system　that　acquires　high-resolution　MS　images.　By　taking　advantage　of　notch　filters　to　block　only　specific　bands　of　the　spectrum,　light　from　most　of　the　spectrum　is　allowed　to　pass　through,　resulting　in　a　high　light　efficiency　imaging　system.　A　compressive　sensing　approach　is　proposed　to　obtain　images　of　high　spatial　and　spectral　resolution.　Trained　sparse　dictionaries　are　used　to　perform　the　spectral　and　spatial　data　super-resolution　of　the　acquired　images.　We　simulated　the　effectiveness　of　our　algorithm　on　a　public　dataset　and　verified　the　imaging　performance　of　the　prototype　system　by　observing　natural　images.　The　experimental　results　show　that　the　spatial　resolution　can　be　improved　threefold　on　the　laboratory　target,　the　spectral　resolution　can　be　improved　from　9　to　31　bands,　and　the　average　peak　signal-to-noise　ratio　remains　at　39.　Our　prototype　imaging　system　can　realize　high　spatial　and　spectral　resolution　imaging　results.　©　2022　SPIE