Image Processing with MATLAB - SPIRO THE TECH GURU

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This tutorial discusses how to use MATLAB for image processing. Some familiarity with MATLAB is assumed (you should know how to use matrices and write an M-file).

It is helpful to have the MATLAB Image Processing Toolbox, but fortunately, no toolboxes are needed for most operations. Commands requiring the Image Toolbox are indicated with.

Image representation

There are five types of images in MATLAB.

Grayscale. A grayscale image M pixels tall and N pixels wide is represented as a matrix of double datatype of size M×N. Element values (e.g., MyImage(m,n)) denote the pixel grayscale intensities in [0,1] with 0=black and 1=white.
Truecolor RGB. A truecolor red-green-blue (RGB) image is represented as a three-dimensional M×N×3 double matrix. Each pixel has red, green, blue components along the third dimension with values in [0,1], for example, the color components of pixel (m,n) are MyImage(m,n,1) = red, MyImage(m,n,2) = green, MyImage(m,n,3) = blue.
Indexed. Indexed (paletted) images are represented with an index matrix of size M×N and a colormap matrix of size K×3. The colormap holds all colors used in the image and the index matrix represents the pixels by referring to colors in the colormap. For example, if the 22nd color is magenta MyColormap(22,:) = [1,0,1], then MyImage(m,n) = 22 is a magenta-colored pixel.
Binary. A binary image is represented by an M×N logical matrix where pixel values are 1 (true) or 0 (false).
uint8. This type uses less memory and some operations compute faster than with double types. For simplicity, this tutorial does not discuss uint8 further.

Grayscale is usually the preferred format for image processing. In cases requiring color, an RGB color image can be decomposed and handled as three separate grayscale images. Indexed images must be converted to grayscale or RGB for most operations.

Reading and writing image files

MATLAB can read and write images with the imread and imwrite commands. Although a fair number of file formats are supported, some are not. Use imformats to see what your installation supports:

When reading images, an unfortunate problem is that imread returns the image data in uint8 datatype, which must be converted to double and rescaled before use. So instead of calling imread directly, I use the following M-file function to read and convert images:

Right-click and save getimage.m to use this M-function. If image baboon.png is in the current directory (or somewhere in the MATLAB search path), you can read it with MyImage = getimage('baboon.png'). You can also use partial paths, for example if the image is in <current directory>/images/ with getimage('images/baboon.png').

To write a grayscale or RGB image, use

imwrite(MyImage,'myimage.png');

Take care that MyImage is a double matrix with elements in [0,1]—if improperly scaled, the saved file will probably be blank.

When writing image files, I highly recommend using the PNG file format. This format is a reliable choice since it is lossless, supports truecolor RGB, and compresses pretty well. Use other formats with caution.

For example, image signal power is used in computing signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR). Given clean image uclean and noise-contaminated image u,

% Compute SNR

snr = -10*log10( sum((uclean(:) - u(:)).^2) / sum(uclean(:).^2) );

% Compute PSNR (where the maximum possible value of uclean is 1)

psnr = -10*log10( mean((uclean(:) - u(:)).^2) );

Be careful with norm: the behavior is norm(v) on vector v computes sqrt(sum(v.^2)), but norm(A) on matrix A computes the induced L² matrix norm,

norm(A) = sqrt(max(eig(A'*A))) gaah!

So norm(A) is certainly not sqrt(sum(A(:).^2)). It is nevertheless an easy mistake to use norm(A) where it should have been norm(A(:)).

Linear filters

Linear filtering is the cornerstone technique of signal processing. To briefly introduce, a linear filter is an operation where at every pixel x_m,n of an image, a linear function is evaluated on the pixel and its neighbors to compute a new pixel value y_m,n.

A linear filter in two dimensions has the general form

y_m,n = ∑_j∑_k h_j,k x_m−j,n−k

where x is the input, y is the output, and h is the filter impulse response. Different choices of h lead to filters that smooth, sharpen, and detect edges, to name a few applications. The right-hand side of the above equation is denoted concisely as h∗x and is called the “convolution of h and x.”

Spatial-domain filtering

Two-dimensional linear filtering is implemented in MATLAB with conv2. Unfortunately, conv2 can only handle filtering near the image boundaries by zero-padding, which means that filtering results are usually inappropriate for pixels close to the boundary. To work around this, we can pad the input image and use the 'valid' option when calling conv2.

A 2D filter h is said to be separable if it can be expressed as the outer product of two 1D filters h1 and h2, that is, h = h1(:)*h2(:)'. It is faster to pass h1 and h2 than h, as is done above for the moving average window and the Gaussian filter. In fact, the Sobel filters hx and hy are also separable—what are h1 and h2?

Fourier-domain filtering

Spatial-domain filtering with conv2 is easily a computaionally expensive operation. For a K×K filter on an M×N image, conv2 costs O(MNK²) additions and multiplications, or O(N⁴) supposing M∼N∼K.

For large filters, filtering in the Fourier domain is faster since the computational cost is reduced to O(N² log N). Using the convolution-multiplication property of the Fourier transform, the convolution is equivalently computed by

% Compute y = h*x with periodic boundary extension

[k1,k2] = size(h);

hpad = zeros(size(x));

hpad([end+1-floor(k1/2):end,1:ceil(k1/2)], ...

[end+1-floor(k2/2):end,1:ceil(k2/2)]) = h;

y = real(ifft2(fft2(hpad).*fft2(x)));

The result is equivalent to conv2padded(x,h) except near the boundary, where the above computation uses periodic boundary extension.

Fourier-based filtering can also be done with symmetric boundary extension by reflecting the input in each direction:

% Compute y = h*x with symmetric boundary extension

xSym = [x,fliplr(x)]; % Symmetrize horizontally

xSym = [xSym;flipud(xSym)]; % Symmetrize vertically

[k1,k2] = size(h);

hpad = zeros(size(xSym));

hpad([end+1-floor(k1/2):end,1:ceil(k1/2)], ...

[end+1-floor(k2/2):end,1:ceil(k2/2)]) = h;

y = real(ifft2(fft2(hpad).*fft2(xSym)));

y = y(1:size(y,1)/2,1:size(y,2)/2);

(Note: An even more efficient method is FFT overlap-add filtering. The Signal Processing Toolbox implements FFT overlap-add in one-dimension in fftfilt.)

Nonlinear filters

A nonlinear filter is an operation where each filtered pixel y_m,n is a nonlinear function of x_m,n and its neighbors. Here we briefly discuss a few types of nonlinear filters.

Order statistic filters

If you have the Image Toolbox, order statistic filters can be performed with ordfilt2 and medfilt2. An order statistic filter sorts the pixel values over a neighborhood and selects the kth largest value. The min, max, and median filters are special cases.

Morphological filters

If you have the Image Toolbox, bwmorph implements various morphological operations on binary images, like erosion, dilation, open, close, and skeleton. There are also commands available for morphology on grayscale images: imerode, imdilate and imtophat, among others.

Build your own filter

Occasionally we want to use a new filter that MATLAB does not have. The code below is a template for implementing filters.

[M,N] = size(x);

y = zeros(size(x));

r = 1; % Adjust for desired window size

for n = 1+r:N-r

for m = 1+r:M-r

% Extract a window of size (2r+1)x(2r+1) around (m,n)

w = x(m+(-r:r),n+(-r:r));

% ... write the filter here ...

y(m,n) = result;

end

(Note: A frequent misguided claim is that loops in MATLAB are slow and should be avoided. This was once true, back in MATLAB 5 and earlier, but loops in modern versions are reasonably fast.)

For example, the alpha-trimmed mean filter ignores the d/2 lowest and d/2 highest values in the window, and averages the remaining (2r+1)²−d values. The filter is a balance between a median filter and a mean filter. The alpha-trimmed mean filter can be implemented in the template as

% The alpha-trimmed mean filter

w = sort(w(:));

y(m,n) = mean(w(1+d/2:end-d/2)); % Compute the result y(m,n)

As another example, the bilateral filter is

y_m,n =	∑_j,k h_j,k,m,n x_m−j,n−k
	∑_j,k h_j,k,m,n

where

h_j,k,m,n = e^{−(j2+k2)/(2σ}_s²⁾ e^−(x_m−j,n−k^− x_m,n^)2/(2σ_d²⁾.

The bilateral filter can be implemented as

% The bilateral filter

[k,j] = meshgrid(-r:r,-r:r);

h = exp( -(j.^2 + k.^2)/(2*sigma_s^2) ) .* ...

exp( -(w - w(r+1,r+1)).^2/(2*sigma_d^2) );

y(m,n) = h(:)'*w(:) / sum(h(:));

If you don't have the Image Toolbox, the template can be used to write substitutes for missing filters, though they will not be as fast as the Image Toolbox implementations.

Filter	Code
medfilt2	y(m,n) = median(w(:));
ordfilt2	w = sort(w(:)); y(m,n) = w(k); % Select the kth largest element
imdilate	% Define a structure element as a (2r+1)x(2r+1) array SE = [0,1,0;1,1,1;0,1,0]; y(m,n) = max(w(SE));