Image processing: Difference between revisions
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Most of the signal processing concepts that apply to one-dimensional signals — such as [[Image_resolution | resolution]], [[dynamic range]], [[bandwidth]], [[filter (signal processing)| filtering]], etc. — extend naturally to images as well. However, image processing brings some new concepts — such as [[connected|connectivity]] and [[rotational invariance]] — that are meaningful or useful only for two-dimensional signals. Also, certain one-dimensional concepts — such as [[differential operators]], [[edge detection]], and [[domain modulation]] — become substantially more complicated when extended to two dimensions. |
Most of the signal processing concepts that apply to one-dimensional signals — such as [[Image_resolution | resolution]], [[dynamic range]], [[bandwidth]], [[filter (signal processing)| filtering]], etc. — extend naturally to images as well. However, image processing brings some new concepts — such as [[connected|connectivity]] and [[rotational invariance]] — that are meaningful or useful only for two-dimensional signals. Also, certain one-dimensional concepts — such as [[differential operators]], [[edge detection]], and [[domain modulation]] — become substantially more complicated when extended to two dimensions. |
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A few decades ago, image processing was done largely in the [[analog]] domain, chiefly by [[optics|optical]] devices. Optical methods are inherently [[parallel]], and for that reason they are still essential to [[holography]] and a few other applications. However, as [[computers]] keep getting faster, analog techniques are being increasingly replaced by [[digital image processing]] techniques — which are more versatile, reliable, accurate, and easier to implement. |
A few decades ago, image processing was done largely in the [[analog (signal)|analog]] domain, chiefly by [[optics|optical]] devices. Optical methods are inherently [[parallel]], and for that reason they are still essential to [[holography]] and a few other applications. However, as [[computers]] keep getting faster, analog techniques are being increasingly replaced by [[digital image processing]] techniques — which are more versatile, reliable, accurate, and easier to implement. |
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==See also== |
==See also== |
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Revision as of 22:25, 27 May 2005
Image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video.
Most of the signal processing concepts that apply to one-dimensional signals — such as resolution, dynamic range, bandwidth, filtering, etc. — extend naturally to images as well. However, image processing brings some new concepts — such as connectivity and rotational invariance — that are meaningful or useful only for two-dimensional signals. Also, certain one-dimensional concepts — such as differential operators, edge detection, and domain modulation — become substantially more complicated when extended to two dimensions.
A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. Optical methods are inherently parallel, and for that reason they are still essential to holography and a few other applications. However, as computers keep getting faster, analog techniques are being increasingly replaced by digital image processing techniques — which are more versatile, reliable, accurate, and easier to implement.
See also
- optics
- photography
- imaging
- computer vision
- computer graphics
- Digital image processing
- digitizing
- super-resolution