# Matlab: removing the notebook line from Paint

As a child, many of us drew a lot of paint. One day our paint notebook ran out of free paper, and we drowned inside a simple vertical notebook. During our image processing course, we will be using Matlab to remove vertical lines from the paper of a notebook.

For removing vertical lines inside painting paper, I use two methods:

• The Fourier transform
• Morphology

I draw my supervisor face with sketch for using a sample for working in this task. Farzin Yaghmaee

Remove vertical line inside painting paper + fourier transform

Based on the spectrom of the image, the painting lines are located on the vertical line and in the middle. My program blacked out this middle line, but left the part of the middle of the spectrom where the rest of the images are intact, as shown above.

```clc;

clear all;

close all;

image_size = size(image);

temp_image_size = size(image_size);

if(temp_image_size(2) > 2)

image = rgb2gray(image);

end

spec_orig = fft2(double(image));

spec_img = fftshift(spec_orig);

figure;

subplot(2,2,1);

imshow(image);

title('original image');

subplot(2,2,2);

spec_img_temp = log(1 + spec_img);

imshow(spec_img_temp,[]);

title('spectrum original image');

for i = 1 : image_size(1)

if(i > round(image_size(1) / 2 - 15) && i < round(image_size(1) / 2 + 15))

continue;

end

for j = round(image_size(2) / 2 - 2) : round(image_size(2) / 2 + 2)

spec_img(i,j) = 0;

end

end

subplot(2,2,3);

ptnfx = ifft2(ifftshift(spec_img));

imshow(uint8(ptnfx));

title('result image');

spec_orig = fft2(ptnfx);

spec_img = fftshift(spec_orig);

subplot(2,2,4);

spec_img = log(1 + spec_img);

imshow(spec_img,[]);

title('spectrum result image');```

result: Remove vertical line inside painting paper + morphology

In the first step, I converted paint images into grayscale color. I used the Matlab function rgb2gray to do this.

Next, I convert grayscale images to binary images. A binary image has two values: 0 or 1. I use the imbinarize function in Matlab to convert these values to binary.

Then, I made three linear windows of the following sizes:

• 3*180
• 2*90
• 5*90

The morphology actions I run are as follows:

• Dilation with a 3*180 linear window.
• Erosion with a 3*90 linear window.
• Dilation with a 5*90 linear window.

Manually selecting and testing these windows. I use imerode for erosion and imdilate for dilation.

I tested median and mean filters inside the result image as a final step. The results were improved by these filters.
For the median and mean filters, I wrote a personal function.

```clc;

clear;

close all;

image_paint = image_raw;

image_size = size(image_paint);

image_x = image_size(1);

image_y = image_size(2);

size_check = size(image_size);

if(size_check(1, 2) > 2)

image_paint = rgb2gray(image_paint);

end

image_bin = imbinarize(image_paint, 'adaptive', 'ForegroundPolarity', 'dark', 'Sensitivity', 0.5);

SE1 = strel('line',6, 180);

SE2 = strel('line',2, 90);

SE3 = strel('line',7, 90);

image_erode = imerode(image_bin, SE2);

image_dilate = imdilate(image_erode, SE1);

image_dilate = imdilate(image_dilate, SE3);

median_result = median(image_dilate, image_x, image_y);

mean_result = mean(image_dilate, image_x, image_y);

figure;

subplot(1,2,1), imshow(image_raw);

title('image raw');

subplot(1,2,2), imshow(image_dilate);

title('image result');```

result: 