Histogram equalization array example

Summary:

Histogram balancing example

Clear all;

Close all;

CLC;

I = [1 5 255 255 100 200 255 200;

17 254 255 100 10 9;

37 10 100 100 2 9 6;

36 10 10 9 2 8 2;

21 8 8 9 3 4 2;

10 7 8 8 3 2 1;

11 8 8 7 2 2 1;

23 9 8 7 2 2 0]

% Iw.imread('fig36.bmp ');

% I = rgb2gray (I );

% Figure (3)

% Imshow (I );

[M, N] = size (I)

I1 = max (I); % calculate the maximum value of each column

IMAX = max (I1) % to find the maximum value

 

I3 = min (I); % calculates the minimum value of each column

Imin = min (I3) % to obtain the minimum value

K (IMAX-Imin) = 4;

HS (IMAX-Imin) = 5;

P = 0

For P = Imin: Imax-1

For j = 1: N

For I = 1: m

If I (I, j) = P

K (p + 1) = K (p + 1) + 1;

End

End

End

P = p + 1;

% Plot the histogram of the original image

Figure (1)

Bar (P, K (p); Hold on;

% Calculate the probability of each pixel value of the original image

HS (p) = K (P)/(m * n );

Sum (HS );

End

% Calculate the cumulative gray distribution of each pixel in the image HP

HP (IMAX-Imin) = 0;

HP (1) = HS (1 );

For x = 2 :( IMAX-Imin)

HP (x) = HS (x) + sum (HP (x-1 ));

If HS (x) = 0

HP (x) = 0;

End

End

HP (1, 255) = 1

% Calculate the gray value of the new image

For y = 1 :( IMAX-Imin)

G (y) = 255 * HP (y );

% Plot the histogram of the balanced image

Figure (2)

Bar (Y, g (y); Hold on;

End

The above error: the code is corrected:

Clear all;
Close all;
CLC;
% I = [1 5 255 255 100 200 255 200;
% 1 7 254 255 100 10 10 9;
% 3 7 10 100 100 2 9 6;
% 3 6 10 10 9 2 8 2;
% 2 1 8 8 9 3 4 2;
% 1 0 7 8 8 3 2 1;
% 1 1 8 8 7 2 2 1;
% 2 3 9 8 7 2 2 0]
I =imread('fig36.bmp ');
% I = rgb2gray (I );
Figure (4)
Imshow (I );
[M, N] = size (I)
I1 = max (I); % calculate the maximum value of each column
IMAX = max (I1) % to find the maximum value

I3 = min (I); % calculates the minimum value of each column
Imin = min (I3) % to obtain the minimum value
K = zeros (1,256 );
HS = zeros (1,256 );

For P = 1:256
K (p) = length (find (I = (p-1 )));
% Calculate the probability of each pixel value of the original image
HS (p) = K (P)/(m * n );
Sum (HS );
P = p + 1;
End
% Plot the histogram of the original image
Figure (1)
Bar (0: 255, HS); Hold on;
Title ('original image histograms ');
Xlabel ('grayscale value ');
Ylabel ('probability of occurrence ');

% Calculate the cumulative gray distribution of each pixel in the image HP
HP = zeros (1,256 );
HP (1) = HS (1 );
For x = 2: 256
HP (x) = HS (x) + sum (HP (x-1 ));
End
% Calculate the gray value of the new image
For Y = 1:256
G (y) = round (256 * HP (y ));
Newgp (y) = sum (HS (find (G = y )));
End
G
Newgp
% Plot the histogram of the balanced image
Figure (2)
Bar (0: 255, newgp );
Title ('histogram after equalization ');
Xlabel ('grayscale value ');
Ylabel ('probability of occurrence ');

Newi = I; % fill in the new gray value of each pixel
For I = 1:256
Newi (find (I = (I-1) = g (I );
End
Figure (3)
Imshow (newi );