MATLAB Image Enhancement Program example

 

1. grayscale transformation Enhancement Program:% Gray Transform
CLC;
I = imread ('pout. tif ');
Imshow (I );
J = imadjust (I, [0.3 0.7], [0 1], 1 );   % Transforms the walues in the % intensity image I to values in J by linealy mapping % values between 0.3 and 0.7 to values between 0 and 1.
Figure;
Imshow (j );
J = imadjust (I, [0.3 0.7], [0 1], 0.5 );   % If gamma is less than 1, Mapping Si weighted toward higher (brighter)% Output values.
Figure;
Imshow (j );
J = imadjust (I, [0.3 0.7], [0 1], 1.5 );   % If gamma is greater than 1, the mapping Si weighted toward lower (darker)% Output values.
Figure;
Imshow (j)
J = imadjust (I, [0.3 0.7], [0 1], 1 );   % If top <bottom, the output image is reversed, as in a photographic negative.
Figure;
Imshow (j );

 2. histogram gray transformation % histogram gray transformation
[X, MAP] = imread ('forest. tif ');
I = ind2gray (x, MAP); % converts the indexed image to a grayscale image
Imshow (I );
Title ('original image ');
Improfile % select a diagonal line with the mouse to display the gray value of the Line Segment
Figure; subplot (121)
Plot (0: 0. 01:1, SQRT (0: 0. 01:1 ))
Axis Square
Title ('square root grayscale transformation function ')
Subplot (122)
Maxnum = double (max (I); % get the maximum value of a two-dimensional array
J = SQRT (double (I)/maxnum); % converts the data type to double, and then performs square root Transformation
The % SQRT function does not support the uint8 type.
J = uint8 (J * maxnum); % converts the data type to uint8
Imshow (j)
Title ('square root transformed image') 3. histogram equalization program example % histgram eaqualization
CLC;
% Clear command window
I = imread ('tire. tif ');
% Reads the image in tire. tif into I
Imshow (I );
% Displays the intensity image I with 256 gray levels
Figure;
% Creates a new figure window
Imhist (I );
% Displays a histogram for the intensity image I
J = histeq (I, 64 );
% Transforms the intensity image I, returning J an intensity
Figure;
% Image with 64 discrete levels
Imshow (j );
Figure;
Imhist (j );
J = histeq (I, 32 );
% Transforms the intensity image, returning in % J an intensity
Figure;
% Image with 32 discrete levels
Imshow (j );
Figure;
Imhist (j); 4. histogram-based program example % histgram regulization
CLC;
% Clear command window
I = imread ('tire. tif ');
% Reads the image in tire. tif into I
J = histeq (I, 32 );
% Transforms the intensity image I, returning in
% J an intensity image with 32 discrete levels
[Counts, X] = imhist (j );
% Displays a histogram for the intensity image I
Q = imread ('ut. tif ');
% Reads the image in tire. tif into I
Figure;
Imshow (Q );
Figure;
Imhist (Q );
M = histeq (Q, counts );
% Transforms the intensity image Q so that
% Histogram of the output image m approximately matches counts
Figure;
Imshow (m );
Figure;
Imhist (m); Procedures for enhancing airspace Filtering1. linear smoothing filtering
I = imread ('ight. tif ');
J = imnoise (I, 'sale & pepper', 0.02 );
Subplot (1, 221), imshow (I)
Title ('original image ')
Subplot (1, 222), imshow (j)
Title ('add prepaster noise image ')
K1 = filter2 (fspecial ('average', 3), j)/255; % apply the 3*3 neighbor window method
Subplot (1, 223), imshow (K1)
Title ('neighborhood average filtered image of 3 X3 Windows ')
K2 = filter2 (fspecial ('average', 7), j)/255; % use the 7*7 neighbor window method
Subplot (1, 224), imshow (K2)
Title ('7x7 neighboring average filtered image ')

 

 

2. Median Filter
The two-dimensional median filter function medfit2 in MATLAB is used to remove pretzels from images.
% Image noise ction with median filter
CLC;
Hood = 3; % filter window
[I, MAP] = imread ('Eight. tif ');
Imshow (I, MAP );
Noisy = imnoise (I, 'sale & pepper', 0.05 );
Figure;
Imshow (noisy, MAP );
Filtered1 = medfilt2 (noisy, [hood]);
Figure;
Imshow (filtered1, MAP );
Hood = 5;
Filtered2 = medfilt2 (noisy, [hood]);
Figure;
Imshow (filtered2, MAP );
Hood = 7;
Filtered3 = medfilt2 (noisy, [hood]);
Figure;
Imshow (filtered3, MAP );3. 4. 8-neighbor mean Filtering Algorithm
% Image noise ction with mean Algorithm
CLC;
[I, MAP] = imread ('Eight. tif ');
Noisy = imnoise (I, 'sale & pepper', 0.05 );
Myfilt1 = [0 1 0; 1 1 1; 0 1 0]; % 4 neighborhood average filter Template
Myfilt1 = myfilt1/9; % template Normalization
Filtered1 = filter2 (myfilt1, noisy );
Imshow (filtered1, MAP );
Myfilt2 = [1 1 1; 1 1 1; 1 1 1];
Myfilt2 = myfilt2/9;
Filtered2 = filter2 (myfilt2, noisy );
Figure;
Imshow (filtered2, MAP); examples of frequency enhancement programs1. low-pass filter
% Lowpass filter
CLC;
[I, MAP] = imread ('Eight. tif ');
Noisy = imnoise (I, 'gaussian, 0.05 );
Imshow (noisy, MAP );
Myfilt1 = [1 1 1; 1 1 1; 1 1 1];
Myfilt1 = myfilt1/9;
Filtered1 = filter2 (myfilt1, noisy );
Figure;
Imshow (filtered1, MAP );
Myfilt2 = [1 1 1; 1 2 1; 1 1 1];
Myfilt2 = myfilt2/10;
Filtered2 = filter2 (myfilt2, noisy );
Figure;
Imshow (filtered2, MAP );
Myfilt3 = [1 2 1; 2 4 2; 1 2 1];
Myfilt3 = filter2 (myfilt3, noisy );
Figure;
Imshow (filtered3, MAP );2. An example of a low-pass filter image in bouwbos
I =imread('saturn.png ');
J = imnoise (I, 'sale & pepper', 0.02 );
Subplot (1, 121), imshow (j)
Title ('noisy original image ')
J = double (j );
F = fft2 (j );
G = fftshift (f );
[M, N] = size (f );
N = 3; D0 = 20;
N1 = floor (M/2); N2 = floor (n/2 );
For I = 1: m;
For j = 1: N;
D = SQRT (i-n1) ^ 2 + (j-n2) ^ 2 );
H = 1/(1 + 0.414 * (D/D0) ^ (2 * n ));
G (I, j) = H * g (I, j );
End
End
G = ifftshift (g );
G = uint8 (real (ifft2 (G )));
Subplot (122), imshow (g)
Title ('third-level Butterworth filtered image ')
 Example of a color enhancement program1. Example of true color enhancement:
% True color image decomposition
CLC;
Rgb1_imread('peppers.png ');
Subplot (1, 221), imshow (RGB)
Title ('original true-color image ')
Subplot (222), imshow (RGB (:,:, 1 ))
Title ('red component of a true color image ')
Subplot (223), imshow (RGB (:,:, 2 ))
Title ('green component of a true color image ')
Subplot (224), imshow (RGB (:,:, 3 ))
Title ('Blue components of a true color image ')
 2. Examples of pseudo-color enhancement:
I = imread ('cameraman. tif ');
Imshow (I );
X = grayslice (I, 16); % thresholds the intensity image I using
% Threshold values 1/16, 2/16 ,....., 15/16, returning an indexed % image in X
Figure;
Imshow (x, hot (16 ));
  3. Example of a fake color enhancement processing program
Extends rgb1_imread('ghost.bmp ');
Imshow (RGB );
Rgbnew (:,:, 1) = RGB (:,:, 3 );
Rgbnew (:,:, 2) = RGB (:,:, 1 );
Rgbnew (:,:, 3) = RGB (:,:, 2 );
Figure;
Subplot (121 );
Imshow (RGB );
Subplot (122 );
Imshow (rgbnew );