This　current　paper　provides　a　data-driven　wavelet　estimator　for　deconvolution　density　model.　Moreover,　we　investigate　the　totally　adaptive　estimations　with　moderately　ill-posed　noises　over　Lp　risk　on　Besov　spaces　Br,qs(R)　.　Compared　with　the　traditional　adaptive　wavelet　estimators,　the　estimation　for　the　case　of　0＜s≤1r　is　considered.　On　the　other　hand,　the　convergence　rate　in　the　region　of　1≤p≤2sr+(2β+1)rsr+2β+1　is　improved　than　that　for　not　necessarily　compactly　supported　density　estimations.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Nature　Switzerland　AG.