Matlab Lecture 4

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# Matlab Lecture 4 - PowerPoint PPT Presentation

Matlab Lecture 4. Spatial Filtering. 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. Summary of Image Types and Numeric Classes.

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### Matlab Lecture 4

Spatial Filtering

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

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
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
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)
Averaging Filtering
• For example, suppose we apply the 3 x 3 averaging filter to an image as follows:

>> 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)

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:
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)
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);

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.
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

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.