1. DREAM PLAN IDEA IMPLEMENTATION 1

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3. 3 Introduction to Image Processing Dr. Kourosh Kiani Email: kkiani2004@yahoo.comkkiani2004@yahoo.com Email: Kourosh.kiani@aut.ac.irKourosh.kiani@aut.ac.ir Email: Kourosh.kiani@semnan.ac.irKourosh.kiani@semnan.ac.ir Web: www.kouroshkiani.comwww.kouroshkiani.com Present to: Amirkabir University of Technology (Tehran Polytechnic) & Semnan University

4. Lecture 04 4

5. Algebraic operations used for images are commonly viewed in two groups; mathematical and logical operations. Image adding, subtracting, dividing and multiplying operations constitute mathematical processing and “AND, OR, NOT” etc. operations forms logical operations. SIMPLE ALGEBRAIC OPERATIONS in IMAGES

6. ……… …a4a3 …a2a1 ……… …b4b3 …b2b1 a4 +b4 a3 +b3 a2 +b2 a1 +b1 + = ……… …a4a3 …a2a1 a4 +10 a3 +10 a2 +10 a1 +10 + = 10

8. Reduce noise (increase SNR) averaging, smoothing... 8 + =

9. I = imread(‘rice.tif’); J = imread(‘cameraman.tif’); K = imadd(I, J); imshow(K) Or i=imread('rice.png'); j=imread('cameraman.tif'); k=i+j; imshow(k);

10. I = imread('kourosh.jpg'); figure(1); imshow(I); I2 = imadd(I, 70); figure(2); imshow(I2); + 70 =

11. Image Averaging  Consider a noisy image g(x,y) formed by the addition of noise  (x,y) to an original image f(x,y) g(x,y) = f(x,y) +  (x,y)

12. Image Averaging  If noise has zero mean and is uncorrelated then it can be shown that = image formed by averaging K different noisy images

13. Image Averaging  Then = variances of g and   Then if K increase, it indicates that the variability (noise) of the pixel at each location (x,y) decreases.

14.  Average multiple images (frames) of the same scene together  Useful for removing noise + +... =

15. X = 255 10 75 44 225 100 Y = 50 50 50 Z = 205 0 25 0 175 50 X = uint8([ 255 10 75; 44 225 100]); Y = uint8([ 50 50 50; 50 50 50 ]); Z = imsubtract(X,Y) Image Subtracting

16. rice = imread('rice.png'); figure (1) imshow(rice); background = imopen(rice, strel('disk', 15)); figure (2) imshow(background); rice2 = imsubtract(rice, background); figure (3) imshow(rice2); - =

17. - 70 = I = imread('kourosh.jpg'); J = imsubtract(I,70); Figure(1), imshow(I), Figure(2), imshow(J) Image Subtracting

18. _ = Digital subtraction angiography (DSA) Image Subtracting

19. Digital subtraction angiography (DSA) Image Subtracting

20. X = 2 10 7 4 25 10 Y = 5 5 5 3 5 6 Z = 10 50 35 12 125 60 X = uint8([ 2 10 7; 4 25 10]); Y = uint8([ 5 5 5; 3 5 6 ]); Z = immultiply(X,Y) Image Multiplying

21. I = imread('moon.tif'); figure(1) imshow(I) J = immultiply(I,0.5); figure(2) imshow(J) * 0.5 =

22. X = 100 20 75 30 25 36 Y = 5 5 5 3 5 6 Z = 20 4 15 10 5 6 X = uint8([ 100 20 75; 30 25 36]) Y = uint8([ 5 5 5; 3 5 6 ]) Z = imdivide(X,Y) Image Dividing

23. I = imread('rice.png'); figure(1), imshow(I); background = imopen(I,strel('disk',15)); figure(2), imshow(background); Ip = imdivide(I,background); figure(3), imshow(Ip, []); ÷ =

24. Image Dividing ÷ 2 = I = imread('rice.png'); J = imdivide(I,2); figure(1), imshow(I) figure(2), imshow(J)

25. Questions? Discussion? Suggestions ?

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