Conventional　multispectral　imaging　systems　based　on　bandpass　filters　struggle　to　record　multispectral　videos　with　high　spatial　resolutions　because　of　their　limited　light　efficiencies.　This　paper　proposes　a　multi-aperture　multispectral　imaging　system　based　on　notch　filters　that　overcomes　this　limitation　by　allowing　light　from　most　of　the　spectrum　to　pass　through.　Based　on　this　imaging　principle,　a　prototype　multi-aperture　multispectral　imaging　system　comprising　notch　filters　was　built　and　demonstrated.　Further,　a　dictionary　learning-　and　total　variation-based　spectral　super-resolution　algorithm　was　developed　to　reconstruct　spectral　images.　The　simulation　results　obtained　using　public　multispectral　datasets　showed　that,　compared　to　the　dictionary　learning-based　spectral　super-resolution　algorithm,　the　proposed　algorithm　reconstructed　the　spectral　information　with　a　higher　accuracy　and　removed　noise,　and　the　verification　experiments　confirmed　the　performance　efficiency　of　the　prototype　system.　The　experimental　results　showed　that　the　proposed　imaging　system　can　capture　images　with　high　spatial　and　spectral　resolutions　under　low　illumination　conditions.　The　proposed　algorithm　improved　the　spectral　resolution　of　the　acquired　data　from　9　to　31　bands,　and　the　average　peak　signal-to-noise　ratio　remained　above　43　dB,　which　is　13　dB　higher　than　those　of　the　state-of-the-art　coded　aperture　snapshot　spectral　imaging　methods.　Simultaneously,　the　frame　rate　of　the　imaging　system　was　up　to　5000　frames/s　under　natural　daylight.