In　this　paper,　we　propose　a　novel　tensor-based　denoising　method　targeting　at　plenoptic　images　which　contain　4D　light　field　(2D　angular　+　2D　spatial)　and　5D　hyperspectral　light　field　(2D　angular　+　2D　spatial　+　1D　spectral).　In　order　to　make　use　of　the　high-dimension　structural　property　of　plenoptic　images,　we　first　generalize　the　intrinsic　tensor　sparsity　measure　to　plenoptic　images　by　extending　the　nonlocal　similarity　from　the　spatial　dimension　to　the　angular　dimension.　Second,　to　eliminate　the　sub-pixel　misalignment　of　different　views,　we　integrate　the　spatial　super-resolution　into　denoising　and　exploit　the　spatial-angular　correlation　by　utilizing　the　nonlocal　similarity　of　the　refined　high-resolution　central　view.　In　the　procedure　of　super-resolution,　we　utilize　an　intensity　consistency　criterion　and　a　coordinate　rationality　criterion　to　facilitate　the　process　of　projection.　The　denoising　performance　can　be　boosted　after　back-projection　performed　on　the　refined　high　-resolution　central　view.　Experimental　results　validate　the　superior　performance　of　the　proposed　method　on　several　plenoptic　image　datasets　in　terms　of　both　subjective　and　objective　quality.