Image processing fundamentals
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This document discusses various image processing techniques in MATLAB including 2D signals like pulses, ramps, steps, and sinusoids as well as their spectra. It covers topics such as frequency domain filtering, noise removal with median filtering, bit plane decomposition, image compression mechanics, and color image display. Other topics mentioned are block processing, vector processing, computation time, and transforms/decompositions.
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![Mechanics of Image Compression
• RGB = imread('autumn.tif');
• I = rgb2gray(RGB);
• J = dct2(I);
• imshow(log(abs(J)),[]), colormap(jet),• imshow(log(abs(J)),[]), colormap(jet),
colorbar
• J(abs(J)<10) = 0;
• K = idct2(J);
• figure, imshow(I)
• figure, imshow(K,[0 255])](https://image.slidesharecdn.com/imageprocessingfundamentals-191017034556/85/Image-processing-fundamentals-15-320.jpg)
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Image processing fundamentals
- 1. Image Processing Fundamentals & MATLABMATLAB B.Chandra Mohan Bapatla Engineering College Bapatla
- 4. 2D Unit Step
- 5. 2D Impulse
- 6. 2D Sinusoid
- 7. 2D Signal and Spectrum
- 8. 2D Sinusoid (Horizontal) and Spectrum
- 9. Frequency Domain Filtering Lowpass Filtering
- 10. Frequency domain Filtering (Highpass Filtering)
- 11. Filtering in Spatial domain (Masking)
- 12. Removing Noise using Median Filtering
- 15. Mechanics of Image Compression • RGB = imread('autumn.tif'); • I = rgb2gray(RGB); • J = dct2(I); • imshow(log(abs(J)),[]), colormap(jet),• imshow(log(abs(J)),[]), colormap(jet), colorbar • J(abs(J)<10) = 0; • K = idct2(J); • figure, imshow(I) • figure, imshow(K,[0 255])
- 17. Other Topics • Block Processing • Vector Processing • Computation Time • Transforms• Transforms • Decompositions