Digital image processing
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Digital image processing is the use of algorithms to perform image processing on digital images.
Digital image processing has the same advantages as digital signal processing in that it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing.
History
Because of the computational load of dealing with images containing millions of pixels, digital image processing was largely of academic interest until the 1970s, when dedicated hardware became available that could process images in real time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and compute-intensive operations.
With the fast computers and signal processors available in the 2000s, digital image processing has become the commonest form of image processing, and is generally used because it is not only the most versatile method, but also the cheapest.
Uses
Digital image processing allows the use of much more complex algorithms for image processing, and hence can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
In particular, digital image processing is the only practical technology for:
Some techniques which are used in digital image processing include:
- Principal components analysis
- Independent component analysis
- Self organizing maps
- Hidden Markov models
- Neural networks
See also
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