In　this　paper,　we　propose　a　novel　deep　convolutional　neural　network　(DCNN)　for　removing　snowflakes　from　light　field　(LF)　images.　We　observe　that　snowflakes　in　LF　images　always　interrupt　slopes　in　background　scenes　in　epipolar　plane　images　(EPIs),　which　means　that　snowflakes　may　be　easily　detected　in　EPIs.　Our　method　takes　3D　EPI　volumes　(i.e.,　stacked　subaperture　views　along　the　same　row　or　column　of　an　LF　image)　as　input.　In　this　way,　our　snowflake　detector　based　on　a　3D　residual　network　with　a　convolutional　long　short-term　memory　(ResNet-ConvLSTM)　can　utilize　both　contextual　information　and　3D　scene　structural　information　to　effectively　detect　snowflakes　of　different　sizes　in　LF　images.　Then,　an　encoder-decoder-based　LF　image　restoration　network　is　proposed　to　restore　the　background　image.　Finally,　extensive　experiments　for　comparison　with　the　state-of-the-art　methods　demonstrate　the　effectiveness　of　our　method　for　challenging　scenes.