Point　cloud　upsampling　aims　to　generate　a　dense　and　uniform　point　set　from　a　sparse　and　irregular　point　set.　The　core　challenge　is　to　accurately　restore　the　geometric　structure　and　local　details.　To　overcome　the　challenge,　this　paper　presents　a　novel　frequency-aware　attention　based　point　cloud　upsampling　approach,　which　combines　graph　filtering　and　channel　attention　based　on　the　detection　of　high　spatial-frequency　components　like　edges　and　contours　in　the　human　visual　system.　To　aggregate　the　features　more　efficiently,　an　intra-feature　and　inter-feature　(I-2-feature)　aggregation　block　and　a　structure　sensitive　transformer　block　are　introduced.　On　one　hand,　the　I-2-feature　aggregation　block　serves　to　create　a　complete　local　representation　of　each　point　by　aggregating　intra　and　inter　features.　On　the　other　hand,　the　structure　sensitive　transformer　block　aims　to　enhance　the　quality　of　the　expanded　point　features　by　capturing　the　global　geometric　structures　and　the　fine　local　details.　Furthermore,　to　improve　the　quality　of　the　coarse　output,　a　multi-scale　spatial　refinement　unit　is　applied,　which　leverages　attentional　feature　fusion　and　multi-scale　attention.　Extensive　qualitative　and　quantitative　results　on　both　synthetic　and　real-scanned　datasets　validate　our　proposed　scheme　outperforms　state-of-the-art　point　cloud　upsampling　methods.