Read an image into the workspace.

I = imread('cameraman.tif');

Scale the image using a coefficient of 1.5 in the linear combination.

J = imlincomb(1.5,I);

Display the original image and the processed image.

imshow(I)

figure
imshow(J)

Read an image into the workspace.

I = imread('cameraman.tif');

Create a low-pass filtered copy of the image.

J = uint8(filter2(fspecial('gaussian'), I));

Find the difference image and shift the zero value to 128 using a linear combination of I and J.

K = imlincomb(1,I,-1,J,128); %K(r,c) = I(r,c) - J(r,c) + 128

Display the resulting difference image.

imshow(K)

Read two grayscale uint8 images into the workspace.

I = imread('rice.png');
J = imread('cameraman.tif');

Add the images using a linear combination. Specify the output as type uint16 to avoid truncating the result.

K = imlincomb(1,I,1,J,'uint16');

Display the result.

imshow(K,[])

This example shows the difference between nesting calls and using linear combinations when performing a series of arithmetic operations on images. To illustrate how imlincomb performs all the arithmetic operations before truncating the result, compare the results of calculating the average of two arrays, X and Y, using nested arithmetic functions and using imlincomb.

Create two arrays.

X = uint8([ 255 0 75; 44 225 100]);
Y = uint8([ 50 50 50; 50 50 50 ]);

Average the arrays using nested arithmetic functions. To calculate the average returned in Z(1,1), the function imadd adds 255 and 50 and truncates the result to 255 before passing it to imdivide. The average returned in Z(1,1) is 128.

Z = imdivide(imadd(X,Y),2)

Z = 2x3 uint8 matrix

   128    25    63
    47   128    75

In contrast, imlincomb performs the addition and division in double precision and only truncates the final result. The average returned in Z2(1,1) is 153.

Z2 = imlincomb(.5,X,.5,Y)

Z2 = 2x3 uint8 matrix

   153    25    63
    47   138    75