NPTEL – Digital Image Processing

By Prof. Sukhendu Das, Department of Computer Science and Engineering, IIT Madras. These videos are part of the National Programme on Technology Enhanced Learning (NPTEL), funded by the Government of India and being carried out by the seven Indian Institutes of Technology and IISc Bangalore as a collaborative project.

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In imaging science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.

Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans).

In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance.

See also

  • Comparison of image processing software
  • Multidimensional systems
  • Near sets
  • Photo manipulation

Further reading

  • Tinku Acharya and Ajoy K. Ray (2006). Image Processing - Principles and Applications. Wiley InterScience. 
  • Wilhelm Burger and Mark J. Burge (2008). Digital Image Processing: An Algorithmic Approach Using Java. Springer-Verlag. ISBN 978-1-84628-968-2. 
  • R. Fisher, K (2002). Springer. ISBN 3-540-67754-2.  Missing or empty |title= (help)
  • Tim Morris (2004). Computer Vision and Image Processing. Palgrave Macmillan. ISBN 0-333-99451-5. 
  • Tony F. Chan and Jackie (Jianhong) Shen (2005). Image Processing and Analysis - Variational, PDE, Wavelet, and Stochastic Methods. Society of Industrial and Applied Mathematics. ISBN 0-89871-589-X. 
  • Milan Sonka, Vaclav Hlavac and Roger Boyle (1999). Image Processing, Analysis, and Machine Vision. PWS Publishing. ISBN 0-534-95393-X. 

External links

  • Lectures on Image Processing, by Alan Peters. Vanderbilt University. Updated 15 September 2011.
  • EURASIP Journal on Image and Video Processing — Open Access journal on Image Processing
  • Image Processing On Line – Open access journal with image processing algorithms, open source implementations and demonstrations
  • Computer Vision Online A good source for source codes, software packages, datasets, etc. related to image processing
  • Image processing is fun! A mathematical image processing blog
  • IMMI - Rapidminer Image Mining Extension - tool for image processing and image mining
  • Population open-source image processing library in C++
  • IPRG Open group related to image processing research resources
  • Image Tools .NET/MONO Batch image processing tool
  • Open source cross-platform image processing framework in C++ with JavaScript support

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