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Digital image processing

From Wikipedia, the free encyclopedia

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.

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Transcription

Contents

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:

Digital image transformations

Filtering

Digital filters are used to blur and sharpen digital images. Filtering can be performed in the spatial domain by convolution with specifically designed kernels (filter array), or in the frequency (Fourier) domain by masking specific frequency regions. The following examples show both methods: [4]

Filter type Kernel or mask Example
Original Image
Affine Transformation Original Checkerboard.jpg
Spatial Lowpass
Spatial Mean Filter Checkerboard.png
Spatial Highpass
Spatial Laplacian Filter Checkerboard.png
Fourier Representation Pseudo-code:

image = checkerboard

F = Fourier Transform of image

Show Image: log(1+Absolute Value(F))

Fourier Space Checkerboard.png
Fourier Lowpass
Lowpass Butterworth Checkerboard.png
Lowpass FFT Filtered checkerboard.png
Fourier Highpass
Highpass Butterworth Checkerboard.png
Highpass FFT Filtered checkerboard.png

Image padding in Fourier domain filtering

Images are typically padded before being transformed to the Fourier space, the highpass filtered images below illustrate the consequences of different padding techniques:

Zero padded Repeated edge padded
Highpass FFT Filtered checkerboard.png
Highpass FFT Replicate.png

Notice that the highpass filter shows extra edges when zero padded compared to the repeated edge padding.

Filtering Code Examples

MATLAB example for spatial domain highpass filtering.

img=checkerboard(20);                           % generate checkerboard
% **************************  SPATIAL DOMAIN  ***************************
klaplace=[0 -1 0; -1 5 -1;  0 -1 0];             % Laplacian filter kernel
X=conv2(img,klaplace);                          % convolve test img with
                                                % 3x3 Laplacian kernel
figure()
imshow(X,[])                                    % show Laplacian filtered 
title('Laplacian Edge Detection')

Affine transformations

Affine transformations enable basic image transformations including scale, rotate, translate, mirror and shear as is shown in the following examples:[5]

Transformation Name Affine Matrix Example
Identity
Checkerboard identity.svg
Reflection
Checkerboard reflection.svg
Scale
Checkerboard scale.svg
Rotate
Checkerboard rotate.svg
where θ = π/6 =30°
Shear
Checkerboard shear.svg

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.

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.[6]

See also

References

  1. ^ 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
  2. ^ Azriel Rosenfeld, Picture Processing by Computer, New York: Academic Press, 1969
  3. ^ "Space Technology Hall of Fame:Inducted Technologies/1994". Space Foundation. 1994. Archived from the original on 4 July 2011. Retrieved 7 January 2010.
  4. ^ Gonzalez, Rafael (2008). Digital Image Processing, 3rd. Pearson Hall. ISBN 9780131687288.
  5. ^ Gonzalez, Rafael (2008). Digital Image Processing, 3rd. Pearson Hall. ISBN 9780131687288.
  6. ^ 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

Further reading

  • 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; Steven L. Eddins (2004). Digital Image Processing using MATLAB. Pearson Education. ISBN 978-81-7758-898-9.
  • Milan Sonka; Vaclav Hlavac; Roger Boyle (1999). Image Processing, Analysis, and Machine Vision. PWS Publishing. ISBN 978-0-534-95393-5.
  • Alhadidi, Basim; Zu'bi, Mohammad H.; Suleiman, Hussam N. (2007). "Mammogram Breast Cancer Image Detection Using Image Processing Functions". Information Technology Journal. 6 (2): 217–221. doi:10.3923/itj.2007.217.221.

External links

This page was last edited on 5 February 2019, at 21:08
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