Digital_Image_Processing

These source code that summarizes the codes that I have gathered while attending Professor Sungho Kim's class (Yeungnam University, Digital Image Processing class) and my personal studies.

0. Outline

all of index have matlab code but some index have no result

1. Install Matlab
1.1 Basic Matrix
2. Image resolution
2.1. Image Resize with Interpolation
3. Blob Labeling
3.1. Noise Reduction
4. Intensity Transform
4.1 Histogram equalization
5. Spatial Filtering
5.1. Spatial Sharpening
6. Furier Series
6.1. Aliasing in Image Resizing
6.2 2-D DFT Fourier Spectrum
6.3 Phase Angles and The Reconstructed
6.4. Frequency Analysis
6.5. Steps for Filtering in the Frequency Domain
6.6. Image Low Pass Filter & High Pass Filter
7. Deep learning

1. Install Matlab

Install Matlab
read Image and show

Image=imread('want_to_read.jpg');
imshow(Image);
title('want_to_read','fontsize',16);

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1.1 Basic Matrix

2. Image resolution

2.1 Image Resize with Interpolation

%builtin function

Image=imread('rice.png'); %load an gray image

Image_NN=imresize(Image,0.4,'nearest');
Image_BL=imresize(Image,0.4,'bilinear');
Image_BC=imresize(Image,0.4,'bicubic');

subplot(1,4,1); imshow(Image); title('origin');
subplot(1,4,2); imshow(Image_NN); title('NN');
subplot(1,4,3); imshow(Image_BL); title('bilinear');
subplot(1,4,4); imshow(Image_BC); title('bicubic');

Result

my impletation(resize func) is in directory source_code/myResizeNN or myResizeBil

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3. Blob Labeling

Result 1

Result 2

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3.1. Noise Reduction

4. Intensity Transform

4.1 Histogram equalization

Result

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5. Spatial Filtering

Result 1

Result 2

Result 3

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5.1. Spatial Sharpening

Logic	Input

Laplacian kernel

K=[0 1 0;
   1 -4 1;
   0 1 0]

before shapen	Result 1

Laplacian kernel

K=[1 1 1;
   1 -8 1;
   1 1 1]

before shapen	Result 2

Gaussian kernel

K=fspecial('gaussian',[5,5],1)

Kernel	Result 3

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6. Furier Series

make sine waves
combine each sine waves
sine waves to square waves

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6.1. Aliasing in Image Resizing

same subsampling

Input	Output

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6.2. 2-D DFT Fourier Spectrum

Result

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6.3. Phase Angles and The Reconstructed

my implement code: doesn't exist. The report exists. but upload the correct code

Result

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6.4. Frequency Analysis

6.5. Steps for Filtering in the Frequency Domain

Given an input image f(x,y) of size MxN, obtain thepadding parameters P and Q : Output1
Form a padded image, fp(x,y) of size PxQ by appending the necessary number of zeros to f(x,y): Output2
Multiply fp(x,y) by (-1)x+y to center its transform: Output3

Input	Output1	Output2	Output3

Compute the DFT, F(u,v) of the image from step 3: Output4
Generate a real, symmetric filter function*, H(u,v), of size PxQ with center at coordinates (P/2, Q/2): Output5
Form the product G(u,v) = H(u,v)F(u,v) using array multiplication: Output6
Obtain the processed image : Output7