In　this　paper　a　novel,　to　the　best　of　our　knowledge,　deep　neural　network　(DNN),　VUR-Net,　is　proposed　to　realize　direct　and　accurate　phase　unwrapping.　The　VUR-Net　employs　a　relatively　large　number　of　filters　in　each　layer　and　adopts　alternately　two　types　of　residual　blocks　throughout　the　network,　distinguishing　it　from　the　previously　reported　ones.　The　proposed　method　enables　the　wrapped　phase　map　to　be　unwrapped　precisely　without　any　preprocessing　or　postprocessing　operations,　even　though　the　map　has　been　degraded　by　various　adverse　factors,　such　as　noise,　undersampling,　deforming,　and　so　on.　We　compared　the　VUR-Net　with　another　two　state-of-the-art　phase　unwrapping　DNNs,　and　the　corresponding　results　manifest　that　our　proposal　markedly　outperforms　its　counterparts　in　both　accuracy　and　robustness.　In　addition,　we　also　developed　two　new　indices　to　evaluate　the　phase　unwrapping.　These　indices　are　proved　to　be　effective　and　powerful　as　good　candidates　for　estimating　the　quality　of　phase　unwrapping.　(C)　2020　Optical　Society　of　America