Light　field,　a　promising　representation　to　describe　the　scene　appearance,　is　susceptible　to　various　noise　due　to　the　current　sensor　design.　This　paper　proposes　a　novel　tensor-based　denoising　method　for　the　4D　light　field　that　consists　of　two　main　steps.　First,　we　generalize　the　intrinsic　tensor　sparsity　measure　to　light　field　images　by　exploiting　the　nonlocal　similarity　across　the　spatial　and　angular　dimensions.　Second,　we　further　exploit　the　spatial-angular　correlation　by　integrating　light　field　super-resolution　into　the　denoising　process　to　eliminate　the　sub-pixel　misalignment　of　different　views.　After　a　back-projection　from　the　refined　high-resolution　central　view　under　an　intensity　consistency　criteria,　the　denoising　performance　for　the　light　field　can　be　boosted.　Experimental　results　validate　the　superior　performance　of　the　proposed　method　in　terms　of　both　PSNR　and　visual　quality　on　the　HCI　light　field　dataset.