Matlab Lecture 4

1. Matlab Lecture 4 Spatial Filtering

2. Image Types in the Toolbox The Image Processing Toolbox supports four: Basic types of images: • Indexed images • RGB images • Intensity images • Binary images Colored Gray-scale Black and white

3. Summary of Image Types and Numeric Classes

5. Spatial Filtering • Filtering in Matlab • Linear filters:usingfspecial(type,parameter);The type could be: • ‘average’ filter • ‘laplacian’ filter • ‘log’ filter • Non-linear filters: • Median filter: • medfilt2

6. Spatial Filtering • We can create our filters by hand, or by using the fspecial function; this has many options which makes for easy creation of many deferent filters. • fspecial(type,parameter)  creates and returns common filters. • filter2(filter,image)  apply the filter on the image

7. Averaging Filtering • fspecial('average',[5,7]) • will return an averaging filter of size 5 x 7 • fspecial('average',11) • will return an averaging filter of size 11 x 11 • fspecial('average') • will return an averaging filter of size 3 x 3 (default)

8. Averaging Filtering • For example, suppose we apply the 3 x 3 averaging filter to an image as follows: >> c=imread('cameraman.tif'); >> f1=fspecial('average'); >> cf1=filter2(f1,c); • We now have a matrix of data type double. To display this, we can do any of the following: • transform it to a matrix of type uint8, for use with imshow. • imshow(uint8(cf1)); • divide its values by 255 to obtain a matrix with values in the 0.1-1.0 range, for use with imshow. • imshow(cf1/255); • use mat2gray to scale the result for display. • imshow(mat2gray(cf1)); >> figure,imshow(c),figure,imshow(cf1/255)

9. Averaging Filtering • will produce the images shown in figures 6.4(a) and 6.4(b). • The averaging filter blurs the image; the edges in particular are less distinct than in the original. • The image can be further blurred by using an averaging filter of larger size. This is shown in the following figures:

10. Averaging Filtering

11. Averaging Filtering

12. Frequencies; low and high pass Filters • High pass filter if it passes over the high frequency components, and reduces or eliminates low frequency components. • Low pass filter if it passes over the low frequency components, and reduces or eliminates high frequency components. • Both are mainly used for noise reduction, sharpen, or smooth the image. • May be used for edge detection. • The output may be the same for the tow types. • sharp  smooth (low), smooth  sharp (high)

13. Frequencies; low and high pass Filters >> f=fspecial('laplacian'); >> cf=filter2(f,c); >> imshow(cf/255); >> f1=fspecial('log'); >> cf1=filter2(f1,c); >> figure,imshow(cf1/255);

14. High Pass Filtering • The images are shown in figure 6.5. Image (a) is the result of the Laplacian filter; image (b) shows the result of the Laplacian of Gaussian (log) filter.

15. Median Filter • medfilt2 • Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. • What is "salt and pepper" noise ? • it is randomly occurring of white and black pixels. n = imnoise(i,'salt & pepper‘,0.2); imshow(n); The default is 0.05 Higher value  more noise

16. Median Filter • B = medfilt2(A) performs median filtering of the matrix A using the default 3-by-3. • Examples • Add salt and pepper noise to an image and then restore the image using medfilt2. >>I = imread('eight.tif'); >>J = imnoise(I,'salt & pepper',0.2); >>K = medfilt2(J); >>imshow(J), figure, imshow(K)