MATLAB Image Basics

Introduction

One nice feature of MATLAB is its provision of handy functions which are not part of the programming language proper. An excellent example of this is its support for images. The base MATLAB product provides routines for the loading from disk, manipulation, display and storing to disk of raster images. While it's true that one can find code libraries to perform these functions for other programming languages, like C++, the MATLAB model offers several advantages, not the least of which is standardization. If I write image-handling code in MATLAB, I know that every other MATLAB user on Earth can run my code without modification or the need for extra header files, libraries, etc. This article will serve as a brief introduction to the use of image data within MATLAB.


Image Data

Images are nearly always stored in digital computers in one of two forms: vector or raster . Vector images store images as line drawings (dots, line segments, polygons) defined by the spatial coordinates of their end points or vertices, and are most often used these days in artistic settings. A raster image is simply a 2-dimensional array of colored pixels (represented by numbers). This article will concentrate on the much more common raster form.

Raster images, being arrays of numbers, are a natural fit for MATLAB, and indeed MATLAB is a convenient tool for applications such as image processing. Raster images always have 2 spatial dimensions (horizontal and vertical), and 1 or more color planes. Typically, grayscale images are stored as a 2-dimensional array, representing 1 color plane with values of 0.0 indicating black, 1.0 indicating white and intermediate values indicating various shades of gray. Color images are similar to grayscale images, but are most often stored as a 3-dimensional array, which is really a stack of three 2-dimensional color planes: one for each primary color: red, green and blue ("RGB"). As with grayscale images, values in the RGB color planes represent brightness of each color. Note that when all three color values are the same, the resulting color is a shade of gray.

For the reader's knowledge, there are also index images which will not be covered here, but which are full of index numbers (integers) which do not represent colors directly, but instead indicate locations in a palette. Also, brightness values are often stored in files as integers, such as 0 - 255 instead of 0.0 to 1.0.


Loading Images from Disk

In MATLAB, images are read from disk using the imread function. Using imread is easy. The basic parameters are the location of the image file and the file format:

>> A = imread('c:\QRNG.png','PNG');
>> whos
Name Size Bytes Class Attributes

A 942 x 1680 x 3 4747680 uint8

This image is 942 pixels vertical by 1680 pixels horizontal, with 3 color planes (red, green and blue). Note that image data has been store in MATLAB as unsigned 8-bit integers (uint8). Since I often make multiple calculations on images, I typically convert the data type to double-precision real (double) and scale to 0.0 - 1.0 (though this will slow calculation):

>> B = double(A) / 255;


Displaying Images

Showing images on the screen is most easily accomplish using the image function:

image(A)

Grayscale images will display using a default palette, which can be changed via the colormap command:

>>colormap gray

Images will be fit to the screen, which may distort their aspect ratio. This can be fixed using:

>>axis equal

...meaning that pixels will use equal scales horizontally and vertically.


Manipulating Images

As arrays, images can be modified using all the fun things we usually do to arrays in MATLAB (subsetting, math operations, etc.). I will mention one other useful base MATLAB tool for image processing: the rgb2hsv function, which converts an RGB image to an HSV one. HSV is a different colorspace (way of representing colors). HSV arrays are similar to RGB arrays, except their 3 color planes are hue, saturation and value (in that order). It is often convenient to work on the value ("brightness") plane, to isolate changes in light/dark from changes in the color. To get back to the land of RGB, use the function hsv2rgb.


Saving Images to Disk

Images can be saved to disk using the imwrite command. This is essentially the inverse of the imread command:

imwrite(A,'New Image.bmp','BMP')

...with the parameters indicating the array to be saved as an image file, the file location and image file format, in that order.

Note that MATLAB understands images as both 0 - 255 uint8s and 0.0 - 1.0 doubles, so there is no need to reverse this transformation before image storage.


Conclusion

Working on images in MATLAB is very convenient, especially when compared to more general-purpose languages. I urge the reader to check the help facility for the functions mentioned here to learn of further functionality.


Further Reading

For more information on image processing, I recommend either of the following books:

Digital Image Processing (3rd Edition) by Gonzalez and Woods
(ISBN-13: 978-0131687288)

Algorithms for Image Processing and Computer Vision by J. R. Parker
(ISBN-13: 978-0471140566)