Digital image processing

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. 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. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

History

Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement.^[1] The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. Images then could be processed 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 computer-intensive operations.

With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing and generally, is used because it is not only the most versatile method, but also the cheapest.

Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in 1994.^[2]

In 2002 Raanan Fattel introduced Gradient domain image processing, a new way to process images in which the differences between pixels are manipulated rather than the pixel values themselves.^[3]

Tasks

Digital image processing allows the use of much more complex algorithms, 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:

• Classification
• Feature extraction
• Pattern recognition
• Projection
• Multi-scale signal analysis

Some techniques which are used in digital image processing include:

• Pixelation
• Linear filtering
• Image editing
• Image restoration
• Principal components analysis
• Independent component analysis
• Hidden Markov models
• Anisotropic diffusion
• Partial differential equations
• Self-organizing maps
• Neural networks
• Wavelets

Applications

Digital camera images

Digital cameras generally include specialized digital image processing hardware – either dedicated chips or added circuitry on other chips – to convert the raw data from their image sensor into a color-corrected image in a standard image file format.^[4] Images from digital cameras can be further processed to improve their quality or to create desired special effects. This additional processing is typically executed by special software programs that can manipulate the images in a variety of ways.

Film

Westworld (1973) was the first feature film to use the digital image processing to pixellate photography to simulate an android's point of view.^[5]

See also

• Computer graphics
• Computer vision
• Digitizing
• GPGPU
• Homomorphic filtering
• Standard test image
• Superresolution
• Multidimensional systems
• IEEE Intelligent Transportation Systems Society
• CVIPtools

References

1. Azriel Rosenfeld, Picture Processing by Computer, New York: Academic Press, 1969
2. "Space Technology Hall of Fame:Inducted Technologies/1994". Space Foundation. 1994. Retrieved 7 January 2010.
3. Bhat, Pravin, et al. "Gradientshop: A gradient-domain optimization framework for image and video filtering." ACM Transactions on Graphics 29.2 (2010): 10.
4. http://www.ti.com/lit/wp/spry105/spry105.pdf
5. A Brief, Early History of Computer Graphics in Film, Larry Yaeger, 16 August 2002 (last update), retrieved 24 March 2010

Further reading

• Solomon, C.J., Breckon, T.P. (2010). Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley-Blackwell. doi:10.1002/9780470689776. ISBN 0470844736.
• Wilhelm Burger and Mark J. Burge (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer. ISBN 978-1-84628-379-6.

• R. Fisher, K Dawson-Howe, A. Fitzgibbon, C. Robertson, E. Trucco (2005). Dictionary of Computer Vision and Image Processing. John Wiley. ISBN 978-0-470-01526-1.

• Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins (2004). Digital Image Processing using MATLAB. Pearson Education. ISBN 978-81-7758-898-9.

• Tim Morris (2004). Computer Vision and Image Processing. Palgrave Macmillan. ISBN 978-0-333-99451-1.

• Milan Sonka, Vaclav Hlavac and Roger Boyle (1999). Image Processing, Analysis, and Machine Vision. PWS Publishing. ISBN 978-0-534-95393-5.

External links

• Lectures on Image Processing, by Alan Peters. Vanderbilt University. Updated 7 January 2016.
• IPRG Open group related to image processing research resources
• Processing digital images with computer algorithms
• IPOL Open research journal on image processing with software and web demos.