Light　field　(LF)　super-resolution　has　achieved　remarkable　results　with　the　assumption　of　only　downsampling.　However,　real-world　LF　scenes　contain　multiple　degradation　effects,　which　makes　it　difficult　for　existing　methods　to　deal　with　hybrid　distortions.　In　this　paper,　we　propose　a　disentangled　feature　distillation　framework　for　LF　super-resolution　with　degradations.　To　reduce　the　learning　difficulty,　we　propose　a　feature　disentanglement　mechanism　to　split　the　mixed　reconstruction　for　both　super-resolution　and　denoising　into　two　single　task　learning　processes.　We　also　propose　a　feature　enhancement　strategy　via　knowledge　distillation　to　transfer　prior　feature　of　each　single　reconstruction　to　our　task　of　mixed　reconstruction.　Finally,　the　separate　restored　representations　are　fused　to　reconstruct　a　clean　high-resolution　LF.　Experiments　demonstrate　the　superior　performance　of　our　framework　for　different　scale　factors　and　noise　levels.　Additionally,　our　approach　can　also　obtain　excellent　performance　for　joint　super-resolution　and　deblurring,　showing　its　gencralization　for　practical　LF　super-resolution　applications.　©　2023　IEEE.