Image processing

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UPIICSA IPN - Binary image

In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.

1 Processing methods
2 Commonly-used signal processing techniques
- 2.1 One-dimensional techniques
- 2.2 Two-dimensional techniques
3 Typical problems
4 Applications
5 Related concepts
6 See also
7 References
8 External links

[edit] Processing methods

UPIICSA IPN - Edge detection

A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. These optical methods are still essential to applications such as holography because they are inherently parallel; however, due to the significant increase in computer speed, these techniques are increasingly being replaced by digital image processing methods.

Digital image processing techniques are generally more versatile, reliable, and accurate; they have the additional benefit of being easier to implement than their analog counterparts. Specialized hardware is still used for digital image processing: computer architectures based on pipelining have been the most commercially successful. There are also many massively parallel architectures that have been developed for the purpose. Today, hardware solutions are commonly used in video processing systems. However, commercial image processing tasks are more commonly done by software running on conventional personal computers.

[edit] Commonly-used signal processing techniques

Most of the signal processing concepts that apply to one-dimensional signals also extend to the two-dimensional image signal. Some of these one-dimensional signal processing concepts become significantly more complicated in two-dimensional processing. Image processing brings some new concepts, such as connectivity and rotational invariance, that are meaningful only for two-dimensional signals.

The Fourier transform, using coherent optics or digital fast fourier transform, is often used for image processing operations involving large-area correlation.

[edit] One-dimensional techniques

Resolution
Dynamic range
Bandwidth
Filtering
Differential operators
Edge detection
Domain modulation
Noise reduction

[edit] Two-dimensional techniques

Connectivity
Rotational invariance

[edit] Typical problems

The red, green, and blue color channels of a photograph by Sergei Mikhailovich Prokudin-Gorskii. The fourth image is a composite.

Geometric transformations such as enlargement, reduction, and rotation
Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space
Registration (or alignment) of two or more images
Combination of two or more images, e.g. into an average, blend, difference, or image composite
Interpolation, demosaicing, and recovery of a full image from a RAW image format like a Bayer filter pattern
Segmentation of the image into regions
Image editing and digital retouching
Extending dynamic range by combining differently exposed images (generalized signal averaging of Wyckoff sets)

and many more.

Besides static two-dimensional images, the field also covers the processing of time-varying signals such as video and the output of tomographic equipment. Some techniques, such as morphological image processing, are specific to binary or grayscale images.

[edit] Applications

Photography and printing
Satellite image processing
Medical image processing
Face detection, feature detection, face identification
Microscope image processing
Car barrier detection

[edit] Related concepts

Classification
Feature extraction
Pattern recognition
Projection
Multi-scale signal analysis
Principal components analysis
Independent component analysis
Self organizing map
Hidden Markov model
Neural networks
Fuzzy logic

[edit] See also

Digital image editing
Digital image processing
Computer graphics
2D computer graphics
Computer vision
GPGPU
optics
photography
Photo manipulation
imaging
digitizing
super-resolution
image differencing
image stabilization
matte (filmmaking)
standard test image
OpenCV
Matrix Vision

[edit] References

Fundamentals of Image Processing by Ian T. Young, Jan J. Gerbrands, Lucas J. Van Vliet, Paperback, ISBN 90 75691 01 7 (1995)
Image Analysis and Mathematical Morphology by Jean Serra, ISBN 0 12 637242 3 (1982)
Image Analysis and Mathematical Morphology, Volume 2: Theoretical Advances by Jean Serra, ISBN 0 12 637241 1 (1988)
Front-End Vision and Multi-Scale Image Analysis by Bart M. ter Haar Romeny, Paperback, ISBN 1 4020 1507 0 (2003)
Geometry-Driven Diffusion in Computer Vision by Bart M. ter Haar Romeny (Ed.), ISBN 0 7293 3087 0 (1994)
Digital Image Processing by Rafael C. Gonzalez, Richard E. Woods, ISBN 0 201 50803 6 (1992)
Digital Image Processing by William K. Pratt, Paperback, ISBN 0 471 01888 0 (1978)

[edit] External links

Categories: Computer vision | Image processing