Neuroimaging　community　usually　employs　spatial　smoothing　to　denoise　magnetic　resonance　imaging　(MRI)　data,　e.　g.,　Gaussian　smoothing　kernels.　Such　an　isotropic　diffusion　(ISD)　based　smoothing　is　widely　adopted　for　denoising　purpose　due　to　its　easy　implementation　and　efficient　computation.　Beyond　these　advantages,　Gaussian　smoothing　kernels　tend　to　blur　the　edges,　curvature　and　texture　of　images.　Researchers　have　proposed　anisotropic　diffusion　(ASD)　and　non-local　diffusion　(NLD)　kernels.　We　recently　demonstrated　the　effect　of　these　new　filtering　paradigms　on　preprocessing　real　degraded　MRI　images　from　three　individual　subjects.　Here,　to　further　systematically　investigate　the　effects　at　a　group　level,　we　collected　both　structural　and　functional　MRI　data　from　23　participants.　We　first　evaluated　the　three　smoothing　strategies＇　impact　on　brain　extraction,　segmentation　and　registration.　Finally,　we　investigated　how　they　affect　subsequent　mapping　of　default　network　based　on　resting-state　functional　MRI　(R-fMRI)　data.　Our　findings　suggest　that　NLD-based　spatial　smoothing　maybe　more　effective　and　reliable　at　improving　the　quality　of　both　MRI　data　preprocessing　and　default　network　mapping.　We　thus　recommend　NLD　may　become　a　promising　method　of　smoothing　structural　MRI　images　of　R-fMRI　pipeline.