By　developing　the　classical　kernel　method,　Delaigle　and　Meister　provide　a　nice　estimation　for　a　density　function　with　some　Fourier-oscillating　noises　over　a　Sobolev　ball　W-2(s)(L)　and　over　L-2　risk　(Delaigle　and　Meister　in　Stat.　Sin.　21:　1065-1092,　2011).　The　current　paper　extends　their　theorem　to　Besov　ball　B-r,q(s)(L)　and　L-p　risk　with　p,　q,　r　is　an　element　of　[1,infinity]　by　using　wavelet　methods.　We　firstly　show　a　linear　wavelet　estimation　for　densities　in　B-r,q(s)(L)　over　L-p　risk,　motivated　by　the　work　of　Delaigle　and　Meister.　Our　result　reduces　to　their　theorem,　when　p　=　q　=　r　=　2.　Because　the　linear　wavelet　estimator　is　not　adaptive,　a　nonlinear　wavelet　estimator　is　then　provided.　It　turns　out　that　the　convergence　rate　is　better　than　the　linear　one　for　r　＜=　p.　In　addition,　our　conclusions　contain　estimations　for　density　derivatives　as　well.