This document is to be distributed for free and without any modification from its original contents. The author declines all responsibility in the damage this document or any of the things you will do with it might do to anyone or to anything. This document (and any of its contents) is not copyrighted and is free of all rights, you may thus use it, modify it or destroy it without breaking any international law.
However, there are [b]no warranties or such[/b] that the content of the document is error proof. If you found an error please check the latest version of this paper on my homepage before mailing me.
According to the author's will, you may not use this document for commercial profit directly, but you may use indirectly its intellectual contents; in which case I would be pleased to receive a mail of notice or even thanks. The algorithms and methods explained in this article are not totally optimised, they are sometimes simplified for pedagogical purposes and you may find some redundant computations or other voluntary clumsiness. Please be indulgent and self criticise everything you might read. Hopefully, lots of this stuff was taken in sources and books of reference; as for the stuff I did: it has proven some true efficiency in test programs I made and which work as wanted. As said in the introduction: If you have any question or any comment about this text, please send it to the above email address, I'll be happy to answer as soon as possible. Finally, notice that all the source code quoted in this article is available here: [url="http://signalprocessing.free.fr/dsp.zip"]http://teachme.free.fr/dsp.zip[/url]. You will need the Allegro graphical library and preferably Microsoft Visual C++ 6.0 to compile the project.
Digital image processing remains a challenging domain of programming for several reasons. First the issue of digital image processing appeared relatively late in computer history, it had to wait for the arrival of the first graphical operating systems to become a true matter. Secondly, digital image processing requires the most careful optimisations and especially for real time applications. Comparing image processing and audio processing is a good way to fix ideas. Let us consider the necessary memory bandwidth for examining the pixels of a 320x240, 32 bits bitmap, 30 times a second: 10 Mo/sec. Now with the same quality standard, an audio stereo wave real time processing needs 44100 (samples per second) x 2 (bytes per sample per channel) x 2 (channels) = 176Ko/sec, which is 50 times less.
Obviously we will not be able to use the same signal processing techniques in both audio and image. Finally, digital image processing is by definition a two dimensions domain; this somehow complicates things when elaborating digital filters.
We will explore some of the existing methods used to deal with digital images starting by a very basic approach of colour interpretation. As a more advanced level of interpretation comes the matrix convolution and digital filters. Finally, we will have an overview of some applications of image processing.
This guide assumes the reader has a basic signal processing understanding (Convolutions and correlations should sound common places) and also some algorithmic notions (complexity, optimisations). The aim of this document is to give the reader a little overview of the existing techniques in digital image processing. We will neither penetrate deep into theory, nor will we in the coding itself; we will more concentrate on the algorithms themselves, the methods. Anyway, this document should be used as a source of ideas only, and not as a source of code. If you have a question or a comment about this text, please send it to the above email address, I'll be happy to answer as soon as possible. Please enjoy your reading.