Image Enhancement in Spatial Domain

Image Enhancement in Spatial Domain Methods (Image Plane)

    • Techniques are based on direct manipulation of pixels in an image
  • Frequency Domain Methods
    • Techniques are based on modifying the Fourier transform of the image.
  • Combination Methods
    • There are some image enhancement in spatial domain techniques based on various combinations of methods from the first two categories

Statistical Order/Non-Linear Filters

Some simple neighbourhood operations include:

Min: Set the pixel value to the minimum in the neighbourhood

Max: Set the pixel value to the maximum in the neighbourhood

Median: The median value of a set of numbers is the midpoint value in that set (e.g. from the set [1, 7, 15, 18, 24] 15 is the median). Sometimes the median works better than the average

Smoothing Spatial Filters

  • One of the simplest spatial filtering operations we can perform is a smoothing operation
  • Simply average all of the pixels in a neighbourhood around a central value
  • Especially useful in removing noise from images
  • Also useful for highlighting gross detail

Image Smoothing Example

Image Smoothing Example

  • The image at the top left is an original image of size 500*500 pixels
  • The subsequent images show the image after filtering with an averaging filter of increasing sizes
  • 3, 5, 9, 15 and 35
  • Notice how detail begins to disappear

Weighted Smoothing Filters

  • More effective smoothing filters can be generated by allowing different pixels in the neighbourhood different weights in the averaging function
  • Pixels closer to the central pixel are more important
  • Often referred to as a weighted averaging
  • By smoothing the original image we get rid of lots of the finer detail which leaves only the gross features for thresholding

Averaging Filter Vs Median Filter Example


  • Filtering is often used to remove noise from images
  • Sometimes a median filter works better than an averaging filter

Strange Things Happen At The Edges!

  • There are a few approaches to dealing with missing edge pixels:
  • Omit missing pixels
  • Only works with some filters
  • Can add extra code and slow down processing
  • Pad the image
  • Typically with either all white or all black pixels
  • Replicate border pixels
  • Truncate the image
  • Allow pixels wrap around the image
  • Can cause some strange image artifacts.
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Santhakumar Raja

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