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　L-p　risk　on　Besov　spaces　B-r,q(s)(R).　Compared　with　the　traditional　adaptive　wavelet　estimators,　the　estimation　for　the　case　of　0　＜　s　＜=　1/r　is　considered.　On　the　other　hand,　the　convergence　rate　in　the　region　of　1　＜=　p　＜=　2sr+(2　beta+1)r/sr+2　beta+1　is　improved　than　that　for　not　necessarily　compactly　supported　density　estimations.