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

image-compress

  • 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.
  1. See More : Image Processing Digital and Analog
  2. See More : What is NoSQL?

About the author

Santhakumar Raja

Hi, This blog is dedicated to students to stay update in the education industry. Motivates students to become better readers and writers.

View all posts

Leave a Reply