Unmanned　Aerial　Vehicles　(UAV)　digital　images　are　often　badly　degraded　by　noise　during　dynamic　acquisition　and　transmission　process.　Denoising　is　very　important　and　difficult　for　UAV-vision　Guided,　because　natural　scene　image　is　complicated　and　having　lots　of　the　edges　and　texture　details.　The　image　denoising　algorithm　based　on　adaptive　dual-tree　discrete　wavelet　packets(ADDWP)　which　combine　the　dual-tree　discrete　wavelet　transform(DDWT)　and　the　wavelet　packets　is　proposed　in　this　paper.　In　ADDWP,　DDWT　subbands　are　further　decomposed　into　wavelet　packets　with　anisotropic　decomposition,　so　that　the　resulting　wavelets　have　elongated　support　regions　and　more　orientations　than　DDWT　wavelets.　To　determine　the　decompoisition　structure,　we　using　the　signal-to-noise　ratio　to　estimate　the　distributing　of　the　denoising　in　order　to　search　the　more　denoising　subbands　to　decomposition　it　again.　So　we　can　get　adaptive　decompoisition　structure　of　wavelet　packets.　The　new　algorithm　has　significantly　lower　computational　complexity　than　a　previously　developed　optimal　basis　selection　algorithm.　For　denoising　the　ADDWP　coefficients,　a　statistical　model　is　used　to　exploit　the　relation　of　the　coefficients　in　order　to　distinguish　the　noise　and　the　signal.　The　proposed　denoising　scheme　gives　better　performance　than　several　state-of-the-art　DDWT-based　schemes　for　images　with　rich　directional　features.　The　visual　quality　of　images　denoised　by　the　proposed　scheme　is　also　superior.