This　paper　discusses　the　uniformly　strong　convergence　of　multivariate　density　estimation　with　moderately　ill-posed　noise　over　a　bounded　set.　We　provide　a　convergence　rate　over　Besov　spaces　by　using　a　compactly　supported　wavelet.　When　the　model　degenerates　to　one-dimensional　noise-free　case,　the　convergence　rate　coincides　with　that　of　Gine　and　Nickl＇s　(Ann.　Probab.,　2009　or　Bernoulli,　2010).　Our　result　can　also　be　considered　as　an　extension　of　Masry＇s　theorem　(Stoch.　Process.　Appl.,　1997)　to　some　extent.