image filetering techniques spatial fileters

image filetering techniques spatial fileters

• 1.
  Image Processing Chapter(3) Part 4:IntensityTransformation and spatial filters Prepared by: Hanan Hardan
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  Spatial filters Remember thattypes of neighborhood: Remember that types of neighborhood:  intensity transformation: neighborhood of size 1x1  spatial filter (or mask ,kernel, template or window): neighborhood of larger size , like 3*3 mask  The spatial filter mask is moved from point to point in an image. At each point (x,y), the response of the filter is calculated Ch3, lesson 6: spatial filters Origin x y Image f (x, y) ( x, y ) Neighbourhood
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  Neighbourhood Operations  Foreach pixel in the origin image, the outcome is written on the same location at the target image. Origin x y Image f (x, y) ( x, y ) Neighbourhood Targe t Origi n
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  Simple Neighbourhood Operations Simpleneighbourhood operations example:  Min: Set the pixel value to the minimum in the neighbourhood  Max: Set the pixel value to the maximum in the neighbourhood
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  The Spatial FilteringProcess j k l m n o p q r Origin x y Image f (x, y) eprocessed = n*e + j*a + k*b + l*c + m*d + o*f + p*g + q*h + r*i Filter (w) Simple 3*3 Neighbourhood e 3 * 3 Filter a b c d e f g h i Original Image Pixels * The above is repeated for every pixel in the original image to generate the filtered image
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  Spatial filters Ch3, lesson5: spatial filters 1.Smoothing Spatial filters [low pass]. 2.Sharpening Spatial Filters[high pass].
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  Spatial filters :Smoothing ( low pass) Ch3, lesson 6: Smoothing filters Use: for blurring and noise reduction. Type of smoothing filters: 1.Standard average 2. weighted average. 3. Median filter linear Order statistics
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  Smoothing Spatial Filters Oneof the simplest spatial filtering operations we can perform is a smoothing operation  Simply average all of the pixels in a neighbourhood around a central value  Especially useful in removing noise from images  Also useful for highlighting gross detail 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 Simple averaging filter
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  Smoothing Spatial Filtering Originx y Image f (x, y) e = 1 /9*106 + 1 /9*104 + 1 /9*100 + 1 /9*108 + 1 /9*99 + 1 /9*98 + 1 /9*95 + 1 /9*90 + 1 /9*85 = 98.3333 Filter Simple 3*3 Neighbourhood 106 104 99 95 100 108 98 90 85 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 1 /9 3 * 3 Smoothing Filter 104 100 108 99 106 98 95 90 85 Original Image Pixels * The above is repeated for every pixel in the original image to generate the smoothed image
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  Spatial filters :Smoothing linear smoothing : averaging kernels Ch3, lesson 6: Smoothing filters Standard average
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  Spatial filters :Smoothing Standard and weighted Average- example Ch3, lesson 6: Smoothing filters 110 120 90 130 91 94 98 200 90 91 99 100 82 96 85 90 Standard averaging filter: (110 +120+90+91+94+98+90+91+99)/9 =883/9 = 98.1 The mask is moved from point to point in an image. At each point (x,y), the response of the filter is calculated
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  What happens whenthe Values of the Kernel Fall Outside the Image !?? Ch3, lesson 6: Smoothing filters
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  First solution :Zeropadding , Ch3, lesson 6: Smoothing filters - ve: black border
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  border padding Ch3, lesson6: Smoothing filters
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  Spatial filters :Smoothing Averaging effects: blurring + reducing noise Ch3, lesson 6: Smoothing filters Original image 3 x 3 averaging 5 x 5 averaging 9 x 9 averaging 15 x 15 averaging 35 x 35 averaging Notice how detail begins to disappear
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  Spatial filters :Smoothing linear smoothing : averaging kernels Ch3, lesson 6: Smoothing filters weighted average . Used to reduce blurring more.
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  Spatial filters :Smoothing Standard and weighted Average- example Ch3, lesson 6: Smoothing filters 110 120 90 130 91 94 98 200 90 91 99 100 82 96 85 90 : Weighted averaging filter : (110 +2 x 120+90+2 x 91+4 x 94+2 x 98+90+2 x 91+99)/16 = The mask is moved from point to point in an image. At each point (x,y), the response of the filter is calculated
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  Spatial filters :Smoothing order statistics: Median filter Ch3, lesson 6: Smoothing filters 110 120 90 130 91 94 98 200 90 95 99 100 82 96 85 90 Steps: 1. Sort the pixels in ascending order: 90,90, 91, 94, 95, 98, 99, 110, 120 2. replace the original pixel value by the median : 95 95 becomes
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  Spatial filters :Smoothing order statistics: Median filter use : blurring + reduce salt and pepper noise The original image with salt and pepper noise The smoothed image using averaging The smoothed image using median Ch3, lesson 6: Smoothing filters
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  Smoothing Filters: MedianFiltering (non-linear)  Very effective for removing “salt and pepper” noise). averaging median filtering
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  Smoothing Filters : Ch3, lesson7: sharpening filters Example1: v=imread('cameraman.tif'); x=imnoise(v,'salt & pepper', 0.02); % added noise to the image h=fspecial('average',[3 3]); % create a two-dimensional filter xx=imfilter(x,h); % apply the filter on the image imshow(x),figure, imshow(xx) Example2: v=imread('cameraman.tif'); x=imnoise(v,'salt & pepper', 0.02); xx=medfilt2(x); imshow(x),figure, imshow(xx) Note: medfilt2 (x,[a b]) where a and b the size of filter.