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Advanced Image Filtering Techniques Using MATLAB: Mean and Edge Detection

This tutorial covers various image processing techniques in MATLAB, focusing on mean filtering and edge detection. It demonstrates how to produce average-filtered images using 9x9 and 25x25 filters, apply Gaussian low-pass filtering with adjustable parameters, and incorporate noise for realistic image processing. The guide also includes methods for noise cleaning, such as removing salt-and-pepper noise, and unsharp masking using Sobel filters. Additionally, it explores edge detection techniques, specifically utilizing the Canny detector and Prewitt operator on sample images.

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Advanced Image Filtering Techniques Using MATLAB: Mean and Edge Detection

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  1. Laboratory of Image Processing Pier Luigi Mazzeo pierluigi.mazzeo@cnr.it July 23, 2014

  2. Filtering

  3. Neighbourhood processing

  4. Mean

  5. Filtering in MatLab

  6. filter2

  7. filter2 (cont.)

  8. fspecial (low pass filters) Produce an Averagefiltering image using 9x9 filter and a 25x25 filter

  9. fspecial (low pass filters) >> hsize = 10; >> sigma = 5; >> h = fspecial(‘gaussian’ hsize, sigma); >> mesh(h); >> imagesc(h); >> outim = imfilter(c, h); >> imshow(outim);

  10. fspecial (low pass filters) for sigma=1:3:10 h = fspecial('gaussian‘, fsize, sigma); out = imfilter(c, h); imshow(out); pause; end

  11. Noise >> noise = randn(size(c)).*sigma; >> output = c+ noise; Periodicnoise

  12. Cleaning salt and pepper noise

  13. fspecial (high pass filters)

  14. fspecial (high pass filters)

  15. Unsharpmasking >> My = fspecial(‘sobel’); >> outim = imfilter(double(c), My); >> imagesc(outim); >> colormap gray; Find the image ‘pelicans.tif’ from internet and use u filter

  16. Cannyedge detector MATLAB: edge(c, ‘canny’); >>help edge I = imread('circuit.tif'); BW1 = edge(I,'prewitt'); BW2 = edge(I,'canny'); figure, imshow(BW1) figure, imshow(BW2)

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