3. Chapter 1
Image Processing

4. Learning Objective
To see how images can be altered by smoothing, noise reduction, contrast and edge enhancement.
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5. Learning Outcomes ALL MUST:
Research and attempt to improve an image using one method.
State the other methods that can be applied.
MOST SHOULD:
Attempt to describe their technique to others.
Use ideas about pixel values to describe changes brought about to an image for their given “process”
SOME COULD:
Apply image processing techniques to “real life” situations and identify where each may be useful.
Grade E
Grade C
Grade A
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6. Zooming in! The value of the binary number gives the shade of grey.
When images are stored, each pixel is represented using a binary code.
100 99 97
185 98
101
The value of the binary number gives the shade of grey.
In coloured images, there are 3 numbers; one for red, one for blue and one for green.
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

7. How can images be processed?
For the purposes of understanding how digital images are manipulated, we are going to consider an 8-bit grayscale image. How many alternatives will that give us? 256 What does this actually mean? Pixel values range from 0 to 255, giving us 256 (28) levels of grey. 0 usually represents white, and 255 represents black.
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

9. Processing Images RANDOM NOISE EDGE
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

10. Changing the Image (Changing the pixel value!)
You could change these numbers by adding to them or multiplying them 8 6
Adding a fixed positive value makes the image brighter but doesn’t change the difference between the light and dark (contrast)
+ 4 4 2 8 4
X 2
Multiplying by a fixed value (>1) makes the image brighter AND changes the contrast
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

11. Mean Smoothing
Take the mean average of all the pixels surrounding each pixel (and the pixel itself). Replace “noisey” pixel. Blurs image.
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

12. Median Smoothing
Take the median average of all the pixels surrounding each pixel (and
the pixel itself). Replace “noisey” pixel.
Removes noise, smooths image.
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

13. Edge Detection
identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities
'Laplace rule‘ Multiply each pixel value by 4, and then subtract the values of the pixels above and below it, and on either side of it. If the result is negative, we treat it as 0
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

14. Questions
1. Which of the methods would be suitable for smoothing sharp edges? Why? 2. Use median smoothing to remove noise from this image of a white cat in a snowstorm  (the black pixels have a value of 255) 4. Why would mean sampling not be appropriate for smoothing the image given in question 3? 5. Use mean smoothing to remove noise from this image of a black cat in a coal cellar 
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.

15. Answers Mean smoothing - median smoothing would not blur the edges.
3. It would produce a really blurred mess, instead of an image, as the noise is too dense. 4.
BY THE END OF THE LESSON ALL MUST: State how images can be processed using a variety of methods.
MOST SHOULD: Use ideas about pixel values to describe changes brought about to an image.
SOME COULD: Apply image processing techniques to “real life” situations and identify where each may be useful.