Module 1.13-Spatial Smoothing Filters.pdf

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1. •Spatialfilteringchangethegreylevelofapixel(x,y)dependingonthe
pixelvaluesinasquareneighborhoodcenteredat(x,y)usinga
matrix(filter,mask,kernel/window).
•Therearemanythingsthatcanbeachievedbyneighborhood
processingwhicharenotpossiblewithpointprocessing.
Image Enhancement
Spatial domain Frequency domain
Point processing Neighbourhood processing
E.g. Negative Image, contrast
stretching, thresholding etc.
E.g. Averaging filter, median filtering
etc.
E.g. Image sharpening using Gaussian
high pass filters, unsharp masking,
highboost filtering etc.

2. Averaging filterWeighted averaging filter
Minimum filterMaximum filterMedian filter
Spatial filters
Smoothing spatial filters (LPF)
Sharpening spatial filters (HPF)
Linear filtersNon-linear(Order statistics) spatial filter
Laplacian linear filter

3. ❖Averaging Linear Filtering:
•Toachieveneighborhoodprocessing,a3x3mask(or5x5,7x7….)
ontheimage,multiplyeachcomponentofthemaskwiththe
correspondingvalueoftheimage,addthemupandplacethevalue
thatweget,atthecenter.Theoperationbeingsameasconvolution.
•Thissmoothingprocessisusedforblurringsharpedges.

4. Input image
Filter mask
Output image
•The two noises are replaced with the average of their surrounding points.
The process of reducing the influence of noise is called smoothing or blurring.