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DREAM PLAN IDEA IMPLEMENTATION 1

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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

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Lecture 04 4

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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

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

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

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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);

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

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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)

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

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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.

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

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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

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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); - =

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

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_ = Digital subtraction angiography (DSA) Image Subtracting

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Digital subtraction angiography (DSA) Image Subtracting

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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

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

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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

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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, []); ÷ =

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

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Questions? Discussion? Suggestions ?

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DREAM PLAN IDEA IMPLEMENTATION 1. 2 3 Introduction to Image Processing Dr. Kourosh Kiani

DREAM PLAN IDEA IMPLEMENTATION 1. 2 3 Introduction to Image Processing Dr. Kourosh Kiani

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