Deep　learning　algorithms　for　pollen　grains　classification　help　monitor　airborne　pollen　grains　and　forecast　the　risks　of　allergic　reactions　but　are　dependent　on　sufficient　and　balanced　image　datasets.　Pollen　grain　image　datasets　are　often　imbalanced　and　deficient.　This　paper　provides　a　series　of　methods　of　pollen　grain　data　augmentation　by　performing　feature　enhancement/attenuation　and　staining　normalization　using　unpaired　translation.　The　BPDD-LM　dataset　was　divided　into　different　domains　based　on　staining　effects　or　visual　features　as　inputs　to　train　the　model.　The　output　images　were　evaluated　by　several　metrics　and　a　classification　neural　network.　The　results　indicated　that　output　images　have　the　desired　transformation　and　can　be　used　to　ameliorate　the　sufficiency　and　balance　of　datasets.　The　methods　can　be　combined　to　increase　the　diversity　of　output　and　still　remain　effective　for　data　augmentation.