Chapter 4: Image Enhancement

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Chapter 4 Image Enhancement

• Introduction and Gray-Scale Modification

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Introduction

• Image enhancement techniques are used to
  emphasize and sharpen image features for display
  and analysis.
• In general, image enhancement is used to generate
  a visually desirable image.
• It can be used as a preprocess or a postprocess.
• Highly application dependent. A technique that
  works for one application may not work for
  another.

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Introduction

• There are three types of image enhancement
  techniques
• Point operations each pixel is modified
  according to a particular equation, independent
  of the other pixels.
• Mask operations each pixel is modified according
  to the values of the pixels neighbors.
• Global operations all the pixel values in the
  image or subimage are taken into consideration.

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Overview of Gray-Scale Modification

• Gray-scale modification methods belong in the
  category of point operations.
• They function by changing the pixels gray-level
  value by using a mapping equation.
• The mapping function maps the original gray-level
  values to other, specified values.
• The primary operations applied to the gray scale
  of an image are to compress or stretch it.

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Overview of Gray-Scale Modification

• Gray-level compression is done to gray-level
  ranges that are of little interest.
• Gray-level stretching is done to gray-level
  ranges where we desire more information.
• The gray-level compression and stretching can be
  illustrate using the graph of modified gray-level
  vs. original gray-level.

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Overview of Gray-Scale Modification
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Overview of Gray-Scale Modification
Original Image
Image after gray-level stretching
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Overview of Gray-Scale Modification

• If the mapping line has a slope between 0 and 1,
  this is called gray-level compression.
• If the slope is greater than one, then it is
  called gray-level stretching.
• In the previous example, the range of gray-level
  values from 28 to 75 is stretched, while other
  gray-level values are left alone.

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Overview of Gray-Scale Modification

• Stretching a particular gray-level range can
  expose a previously hidden visual info.
• In some cases, we may want to stretch a specific
  range of gray levels, while clipping the values
  at the low and high ends.
• The effect of doing this is that the contrast of
  the image is enhanced.

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Overview of Gray-Scale Modification
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Overview of Gray-Scale Modification
Original Image
Image after gray-level stretching
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Overview of Gray-Scale Modification

• Another type of mapping equation is called the
  intensity-level slicing.
• Used for feature extraction.
• Here we select specific gray-level values of
  interest and map them to a specified, typically
  higher, value.
• Using this method, we can bring out the feature
  of interest in the image.

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Overview of Gray-Scale Modification
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Overview of Gray-Scale Modification
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Overview of Gray-Scale Modification
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Histogram Modification

• An alternate perspective to gray-level
  modification that performs a similar function is
  referred to as histogram modification.
• The gray-level histogram of an image is the
  distribution of the gray levels in an image.
• The characteristics of an image can be determined
  from its histogram (refer to Histogram Features
  in Chapter 2).

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Histogram Modification
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Histogram Modification

• The histogram can be modified by a mapping
  function which will stretch, shrink or slide the
  histogram.
• This will change the contrast or brightness of
  the image.
• The graphical representation of histogram
  stretch, shrink and slide can be seen in the
  following diagrams.

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Histogram Modification
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Histogram Modification
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Histogram Modification
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Histogram Modification

• The mapping equation for histogram stretch can be
  found as follows

I(r,c)MAX is the largest gray level value in the
image I(r,c) I(r,c)MIN is the smallest gray level
value in I(r,c) MAX and MIN correspond to the
maximum and minimum gray-level values of the new
range.
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Histogram Modification

• This equation will take an image and stretch the
  histogram across the entire gray-level range.
• This will increase the contrast of a low-contrast
  image.
• If a stretch is desired over a smaller range,
  different MAX and MIN values can be specified.

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Histogram Modification
Low-contrast image
Histogram of low-contrast image
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Histogram Modification
Image after histogram stretching
Histogram of image after stretching
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Histogram Modification

• If most of the pixel values in an image fall
  within a small range, but a few outliners force
  the histogram to span the entire range, a pure
  histogram stretch will not improve the image.
• In this case, it is useful to allow a small
  percentage of the pixel values to be clipped at
  the low and high end of the range.

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Histogram Modification
Original Image
Histogram of the original image
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Histogram Modification
Image after histogram stretching without clipping
Histogram of the image
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Histogram Modification
Image after histogram stretching with clipping 3
low and high value
Histogram of the image
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Histogram Modification

• The opposite of histogram stretch is a histogram
  shrink, which will decrease image contrast by
  compressing the gray levels.
• The histogram shrinking equation is generally the
  same as the one for stretching.
• But for histogram shrinking, MAX and MIN should
  be set to the maximum and minimum of the new,
  compressed range.

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Histogram Modification
Original image
Histogram of original image
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Histogram Modification
Histogram of the image
Image after histogram shrink to the range 75,
175
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Histogram Modification

• In general, histogram shrink reduces contrast and
  may not seem to be useful as image enhancement
  tool.
• However, there is an image-sharpening technique
  algorithm that uses the histogram shrink process
  as a part of the enhancement technique.

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Histogram Modification

• The histogram slide technique can be used to make
  an image either darker or lighter.
• Darker slide histogram towards low end.
• Lighter slide histogram towards high end.
• Histogram slide is done by adding or subtracting
  a fixed number from all the gray-level values.

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Histogram Modification

• Any values slid past the minimum or maximum
  values will be clipped to the respective minimum
  and maximum.
• A positive OFFSET will increase the overall
  brightness.
• A negative OFFSET will create a darker image.

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Histogram Modification
Original image
Histogram of original image
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Histogram Modification
Image after positive-value histogram sliding
Histogram of image after sliding
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Histogram Modification

• Histogram equalization is a popular technique for
  improving the appearance of a poor image.
• Its function is similar to that of histogram
  stretch but often provides more visually pleasing
  results across a wider range of images.

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Histogram Modification

• The histogram equalization process consists of
  four steps
• Find the running sum of the histogram values.
• Normalize the values from step 1 by dividing by
  the total number of pixels.
• Multiply the values from step 2 by the maximum
  gray level value and round.
• Map the gray-level values to the result from step
  3 using one-to-one correspondence.

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Histogram Modification

• Example You are given a 3 bits/pixel image with
  the following histogram
• Next, perform the four steps histogram
  equalization process as mentioned before. The
  result can be seen in the tables in the next
  slide.

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Histogram Modification
The first three steps
The fourth step
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Histogram Modification
Original image
Histogram of original image
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Histogram Modification
Image after histogram equalization
Histogram after equalization
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Histogram Specification

• Sometimes, it is useful to be able to define a
  histogram and modify the histogram of the
  original image to match the histogram that we
  define.
• Such as process is called histogram
  specification.
• This process can be implemented in 4 steps

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Histogram Specification

• Find the mapping table to histogram-equalize the
  image (this is basically the result of histogram
  equalization).
• Specify the desired histogram.
• Find the mapping table to histogram-equalize the
  values of the desired histogram (this is done by
  applying histogram equalization to the specified
  histogram in step 2).
• Map the original values to the values from step 3.

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Histogram Specification
Step 1 Use histogram equalization result from
last example
Step 2 Specify the desired histogram
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Histogram Specification
Step 3 Find the histogram equalization mapping
table for the desired histogram
Step 4 Map the original values to the values
from step 3
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Adaptive Contrast Enhancement

• Adaptive Contrast Enhancement (ACE) filter is
  used with an image with uneven contrast.
• In this case, we want to adjust the contrast
  differently in different regions of the image.
• Regions with low contrast should be given more
  contrast compared to other regions.
• This is different from image modification
  techniques, which are based only on global
  parameters.

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Adaptive Contrast Enhancement

• ACE works by using both the local and global
  image statistics to determine the amount of
  contrast adjustment required.
• The image is processed using the sliding window
  concept.
• The local image statistics are found by
  considering only the current window.
• The global statistics are found by considering
  the entire image.

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Adaptive Contrast Enhancement

• The ACE equation is as follows
• mI(r,c) is the mean for the entire image I(r,c)
• sl local standard deviation (in the window)
• ml local mean (average in window)
• k1, k2 constants, vary between 0 and 1

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Adaptive Contrast Enhancement

• This filter subtracts the local mean from the
  original data and weights the result by the local
  gain factor k1mI(r,c)/sl(r,c).
• This has the effect of intensifying local
  variations.
• Can be controlled by the constant k1.
• Areas of low contrast (low values of sl(r,c)) are
  boosted.

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Adaptive Contrast Enhancement

• The mean is then added back to the result,
  weighted by k2 to restore the local average
  brightness.
• In practice, it is often helpful to shrink the
  histogram of image before applying this filter.
• It is also helpful to limit the range of the
  local gain factor, i.e. set a minimum and maximum
  for the local gain factor.

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Adaptive Contrast Enhancement
Original Image
Histogram equalized version of original image
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Adaptive Contrast Enhancement
Image after being applied with ACE filter. k1
0.9, k2 0.5 Local gain max 25
Histogram equalized version of ACE filtered image