Incident　light　captured　by　an　optical　imaging　system　engenders　multidimensional　high-level　optical　information,　such　as　spatial　resolution,　light　intensity,　spectrum,　and　polarization.　However,　panchromatic　image　sensors　can　only　capture　spatial　and　light　intensity　information.　This　article　presents　a　novel　multispectral-polarization　imaging　approach　that　overcomes　the　limitations　related　to　low　light　flux　and　slow　imaging　speed　found　in　multispectral-polarization　techniques　based　on　bandpass　filters.　By　utilizing　notch　filters　and　a　camera　array　and　integrating　computational　imaging　algorithms,　this　method　achieves　high　temporal,　spatial,　and　spectral　resolution　in　polarization　imaging　for　the　first　time.　The　spatial,　light　intensity,　spectral,　and　polarization　information　of　the　target　scene　can　be　obtained　synchronously　while　guaranteeing　high　light　efficiency　performance　of　the　system.　To　obtain　high　spectral　resolution　polarization　images,　a　K-times　singular　value　decomposition　(K-SVD)　algorithm　is　used　to　extract　the　feature　dictionary　of　the　hyperspectral　dimension.　Then,　a　compressive　sensing-based　algorithm　is　applied　to　conduct　spectral　super-resolution.　The　principle　of　compressive　sensing　is　combined　with　prior　sparsity　and　smoothness　information　to　perform　spectral　super-resolution.　The　performance　of　the　proposed　multispectral-polarization　imaging　system　and　the　effectiveness　of　the　proposed　method　were　verified　using　a　public　multispectral　dataset　and　field　experiments.　The　experimental　results　confirm　that　the　proposed　technology　can　obtain　high-quality　spatial　resolution,　light　intensity,　spectral,　and　polarization　data　of　the　target　scene　in　a　single　shot.　The　algorithm　can　reconstruct　16　spectral　band　images　from　four　captured　spectral　images　while　adequately　preserving　the　polarization　information　of　the　target　scene　in　the　calculated　spectral　imaging　results.