1. Technique 6: General gray-level transformations
USE: To combine thresholding, bunching and splitting into one operation. This is also useful as a single tool in a set of software tools for image processing, since it does anyone of the technique on any part of the histogram.
OPERATION: A function F(g) can be specified which transforms any gray level g to a new gray level q.
Original histogram
Final histogram
F(g) transform
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2. Histogram equalization
Technique 7: Histogram equalization
USE: To improve contrast of an image. Can be used on a whole image or just on a part of an image.
THEORY:
where G is total number of gray levels.
OPERATION:
if t(g) is the actual number of pixels at old gray level g or less, then
Image Processing, Lecture #6
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3. Histogram equalization example
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4. Example
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5. Example
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6. Multi-image operations
Occasionally, it is useful to prepare a new image from a number of previous images.
Example: white shape on white paper
problem: difficult to distinguish between the shape and the paper due to bad lighting.
solution 1: improve the lighting
solution 2: capture an extra image of pure white paper without shape under the same lighting, then subtract the two images.
Technique 8: Background subtraction
USE: To remove light shading or discover movement from one image to another.
THEORY: Gray level in each pixel in one image is subtracted from the gray level in the corresponding pixel in other image, i.e.
result = x-y (x >= y)
If x < y the result is negative: -1 is held as 255
-2 is held as 254
and so on
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7. Technique 8 cont’d A better operation: result = | x - y |
OPERATION: Collect both images. One image, x, should be on a pure background. The image with object will form image y. For each pixel calculate
abs(x-y)
This will give a negative image of the object. To return the image to a positive, use the following formula
new image = MAX - abs(x-y)
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9. Multi-image averaging
A series of poor images of the same scene can be used to find a better image.
Averaging or filtering can be used to enhance the picture.
Multi-image modal filtering
Chose the most popular gray level for each corresponding pixel in a sequence of images
Multi-image median filtering
Similar to modal filtering, except the middle value is chosen
sorting is needed
Multi-image averaging filtering
Using a weighted averaging technique
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10. Technique 9 Technique 9: Recursive filtering
USE: To improve a sequence of still images--reducing noise by producing new images that correspond to weighted averages of previous images.
OPERATION: It is assumed that newly collected images are available from a frame store with a fixed delay between each image.
1. Setting up:
copy an image into a separate frame store, dividing all the gray levels by an integer, n. Add to that image n-1 subsequent images, the gray levels of which are also divided by n. You should now have the average of the first n images in the frame store.
2. Recursion:
For every new image, multiply the contents of the frame store by (n-1)/n and the new image by 1/n, add them together and put the result back into the frame store.
THEORY: At setup the frame store contains
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11. Recursive filtering cont’d
Then after many images the following steady state is created:
n=10 n=5 n=2
new one
last one
last but
last but
last but
last but
last but
last but
last but
last but
last but
and so on
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