In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images.[1] 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.[2] 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.[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:
Some techniques which are used in digital image processing include:
- Anisotropic diffusion
- Hidden Markov models
- Image editing
- Image restoration
- Independent component analysis
- Linear filtering
- Neural networks
- Partial differential equations
- Pixelation
- Principal components analysis
- Self-organizing maps
- Wavelets
Digital image transformations
- Filtering
- Image padding in Fourier domain filtering
- Filtering Code Examples
- Affine transformations
Applications
- Digital camera images
- Film
See also
References
- Pragnan Chakravorty, "What Is a Signal? [Lecture Notes]," IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 175-177, Sept. 2018. https://doi.org/10.1109/MSP.2018.2832195
- Jump up^ Azriel Rosenfeld, Picture Processing by Computer, New York: Academic Press, 1969
- Jump up^ "Space Technology Hall of Fame:Inducted Technologies/1994". Space Foundation. 1994. Archived from the original on 4 July 2011. Retrieved 7 January 2010.
- Jump up^ Gonzalez, Rafael (2008). Digital Image Processing, 3rd. Pearson Hall. ISBN 9780131687288.
- Jump up^ Gonzalez, Rafael (2008). Digital Image Processing, 3rd. Pearson Hall. ISBN 9780131687288.
- Jump up^ A Brief, Early History of Computer Graphics in Film Archived 17 July 2012 at the Wayback Machine., Larry Yaeger, 16 August 2002 (last update), retrieved 24 March 2010
Theory：Detection theory Discrete signal Estimation theory Nyquist–Shannon sampling theorem
Sub-fields：Audio signal processing Digital image processingSpeech processing Statistical signal processing
Techniques：Advanced Z-transform Bilinear transform Constant-Q transform Discrete Fourier transform (DFT) Discrete-time Fourier transform (DTFT) Impulse invariance Integral transform Laplace transform Matched Z-transform method Post's inversion formula Starred transform Z-transform Zak transform
Sampling：Aliasing Anti-aliasing filter Downsampling Nyquist rate / frequency Oversampling Quantization Sampling rate Undersampling Upsampling