Digital Image Processing


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.

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).

Image processing is the only practical technology for:

  • Classification
  • Feature extraction
  • Pattern recognition
  • Projection
  • Multi-scale signal analysis

Some techniques which are used in image processing include:

  • Pixelation
  • Linear filtering
  • Components analysis
  • Hidden Markov models
  • Self-organizing maps
  • Neural networks
  • Wavelets
  • Partial differential equations

Academic Year: 2017-18

Course Detail

Syllabus for Internal Examination

  • Digital Image Fundamentals: Light and Electromagnetic spectrum, Components of Image processing system, Image formation and digitization concepts, Neighbours of pixel adjacency connectivity, regions and boundaries, Distance measures, Applications.
  • Point Processing Operations: Basic gray level transformations, Histogram processing, Using arithmetic/Logic operations
  • Filtering in Spatial Domain: Smoothing spatial filters, Sharpening spatial filters
  • Image Restoration: Various noise models, image restoration using spatial domain filtering, image restoration using frequency domain filtering, Estimating the degradation function, Inverse filtering.

Datasets for Research:

Web Resources: