Hyperspectral　light　field　(HSLF)　images　with　enriched　spectral　and　angular　information　provide　better　representation　of　real　scenes　than　conventional　2D　images.　In　this　paper,　we　propose　a　novel　denoising　method　for　HSLF　images.　The　proposed　method　consists　of　two　main　steps.　First,　we　generalize　the　intrinsic　tensor　sparsity　(ITS)　measure　previously　used　for　3D　hyperspectral　image　denoising　to　the　5D　HSLF,　by　using　the　global　correlation　along　the　spectral　dimension　and　the　nonlocal　similarity　across　the　spatial　and　angular　dimensions.　Second,　we　further　exploit　the　spatial-angular　correlation　by　integrating　light　field　super-resolution　(SR)　into　the　denoising　process.　In　this　way,　the　5D　HSLF　can　be　better　recovered.　Experimental　results　validate　the　superior　performance　of　the　proposed　method　in　terms　of　both　objective　and　subjective　quality　on　a　self-collected　HSLF　dataset,　in　comparison　with　directly　applying　the　state-of-the-art　denoising　methods.