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Normalize Intensities
To counter the effects of bad lighting we have to normalize the image. Image normalization attempts
to spread the pixel intensities over the entire range of intensities. Thus if you have a very dark
image the resulting normalization process will replace many of the dark pixels with lighter pixels
while keeping the relative positions the same, i.e. two pixels may be made lighter but the darker
of the two will still be darker relative to the second pixel.
By evenly distributing the image intensities other image processing functions like thresholding which
are based on a single cutoff pixel intensity become less sensitive to lighting.
For example, the following images from the previous page show what happens during normalization. It is
important to note that any image transformation that is meant to improve bad images must also
preserve already
good ones. In testing image processing functions be sure to always understand the negative side effects
that any function can have.
Normalization did not create much change because image was lighted ok to begin with.
The bad image experienced a large amount of change as the image intensities did not cover the entire
intensity range due to bad lighting. You can see from the histogram that the image intensities are
now more evenly distributed.
Also you can note how the new histogram appears to be not as solid as the original. This is due to how
the intensity values are stretched. Since the new image has exactly the same number of pixels as
the old image the new image still has many pixels intensity values that do not exist and therefore
show up as gaps in the histogram. Adding another filter like a mean blur would cause the
histogram to become more solid again as the gaps would be filled due to smoothing of the image.
Next we need to start focusing on extracting the actual lines in the images.
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