JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Exploring Image Processing and Image Restoration Techniques
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Exploring Image Processing and Image Restoration Techniques
Omarov, Batyrkhan Sultanovich; Altayeva, Aigerim Bakatkaliyevna; Cho, Young Im;
  PDF(new window)
 Abstract
Because of the development of computers and high-technology applications, all devices that we use have become more intelligent. In recent years, security and surveillance systems have become more complicated as well. Before new technologies included video surveillance systems, security cameras were used only for recording events as they occurred, and a human had to analyze the recorded data. Nowadays, computers are used for video analytics, and video surveillance systems have become more autonomous and automated. The types of security cameras have also changed, and the market offers different kinds of cameras with integrated software. Even though there is a variety of hardware, their capabilities leave a lot to be desired. Therefore, this drawback is trying to compensate by dint of computer program solutions. Image processing is a very important part of video surveillance and security systems. Capturing an image exactly as it appears in the real world is difficult if not impossible. There is always noise to deal with. This is caused by the graininess of the emulsion, low resolution of the camera sensors, motion blur caused by movements and drag, focus problems, depth-of-field issues, or the imperfect nature of the camera lens. This paper reviews image processing, pattern recognition, and image digitization techniques, which will be useful in security services, to analyze bio-images, for image restoration, and for object classification.
 Keywords
Image processing;Image restoration;Image enhancement;Super-resolution;Object classification;
 Language
English
 Cited by
 References
1.
M.Petrou and C.Petrou, Image Processing: The Applications. London: Springer-Verlag London Limited., pp.3-10, 2008,.

2.
R.Szeliski, Computer Vision: Algorithms and Applications. London: Springer-Verlag London Limited., pp.3-10, 2008.

3.
G.Bradski and A. Kaehler, Learning OpenCV. Sebastopol, CA: O’Reilly, p.1, 2008.

4.
G.Bradski and A. Kaehler, Learning OpenCV. Sebastopol, CA: O’Reilly, p.10, 2008.

5.
M. Petrou and P. Bosdogianni, Image processing, 2nd ed. Chichester

6.
: Wiley, p.293, 2010.

7.
R.Gonzalez and R.Woods, Digital Image Processing, 2nd ed. New Jersey: Prentice Hall, p.221, 2002.

8.
J.Parker, Algorithms for image processing and computer vision, 2nd ed. New York: Wiley Computer Pub., p.252, 2010,.

9.
M.Petrou and C. Petrou, Image Processing: The Fundamentals, 2nd ed. JohnWiley&Sons, Ltd., p.13, 2010.

10.
R.Gonzalez and R.Woods, Digital Image Processing, 2nd ed. New Jersey: Prentice Hall, p.222, 2002.

11.
J.Parker, Algorithms for image processing and computer vision, 2nd ed. New York: Wiley Computer Pub., p.253, 2010.

12.
R.Gonzalez and R.Woods, Digital Image Processing, 2nd ed. New Jersey: Prentice Hall, pp.262-263, 2002.

13.
R.Gonzalez, R.Woods and S. Eddins, Digital Image processing using MATLAB. Gatesmark Publishing, p.187, 2009.

14.
R. Gonzalez, R. Woods and S. Eddins, Digital Image processing using MATLAB. Gatesmark Publishing, p.199, 2009.

15.
R.Szeliski, Computer Vision: Algorithms and Applications, 1st ed. Washington: Springer, pp.145-146, 2015.

16.
Cambridgeincolour.com, ’Understanding Digital Image Interpolation’, 2015.

17.
J.Simpkins and R.Stevenson, ’An Introduction to Super-Resolution Imaging’, M.S., in Electrical Engineering, University of Notre Dame, 2012.

18.
J.Parker, Algorithms for image processing and computer vision, 2nd ed. New York: Wiley Computer Pub., p.286, 2010.

19.
Docs.opencv.org, ’Cascade Classification — OpenCV 2.4.11.0 documentation’

20.
Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian. Bm3d frames and variational image deblurring. Image Processing, IEEE Transactions on, vol.21, no.4, pp.1715–1728, 2012. crossref(new window)

21.
Kang M. G. Park S. C., Park M. K. Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine, no.20, pp.21–36, 2013.