High-frame-rate　plane　wave　(PW)　imaging　suffers　from　unsatisfactory　image　quality　due　to　the　absence　of　focus　in　transmission.　Although　coherent　compounding　from　tens　of　PWs　can　improve　PW　image　quality,　it　in　turn　results　in　a　decreased　frame　rate,　which　is　limited　for　tracking　fast　moving　tissues.　To　overcome　the　trade-off　between　frame　rate　and　image　quality,　we　propose　a　progressively　dual　reconstruction　network　(PDRN)　to　achieve　adaptive　beamforming　and　enhance　the　image　quality　via　both　supervised　and　transfer　learning　in　the　condition　of　single　or　a　few　PWs　transmission.　Specifically,　the　proposed　model　contains　a　progressive　network　and　a　dual　network　to　form　a　close　loop　and　provide　collaborative　supervision　for　model　optimization.　The　progressive　network　takes　the　channel　delay　of　each　spatial　point　as　input　and　progressively　learns　coherent　compounding　beamformed　data　with　increased　numbers　of　steered　PWs　step　by　step.　The　dual　network　learns　the　downsampling　process　and　reconstructs　the　beamformed　data　with　decreased　numbers　of　steered　PWs,　which　reduces　the　space　of　the　possible　learning　functions　and　improves　the　model＇s　discriminative　ability.　In　addition,　the　dual　network　is　leveraged　to　perform　transfer　learning　for　the　training　data　without　sufficient　steered　PWs.　The　simulated,　in　vivo,　vocal　cords　(VCs),　and　public　available　CUBDL　dataset　are　collected　for　model　evaluation.　©　2022　Elsevier　B.V.