The document discusses various techniques used for image enhancement, including contrast stretching, gray level slicing, and histogram equalization. It provides examples and explanations of how each technique works to improve image quality by increasing contrast, highlighting certain features, and spreading out the histogram to make use of a wider range of grayscale values. The techniques can help with applications in fields like forensics, medical imaging, and astronomy by improving visibility of details and revealing hidden features. While enhancement provides benefits, over-processing is warned against as it can result in loss of information or an artificial appearance.

1. INDIAN INSTITUTE OF TECHNOLOGY ROORKEE
EEN-628
Image enhancement and techniques used for
Image enhancement
Presented by:
Baldeep Singh Chhabra
19115039

2. 2
Outline
• Introduction
• Techniques used in Image Enhancement
• Contrast Stretching
• Gray level slicing
• Histogram Equalization
• Advantages of Image Enhancement
• Disadvantages
• Applications of Image Enhancement
• Summary

3. 3
Introduction
• The principal objective of image enhancement is to process a
given image so that the result is more suitable than the original
image for a specific application.
• It accentuates or sharpens image features such as edges,
boundaries, or contrast to make a graphic display more helpful
for display and analysis.
• The enhancement doesn't increase the inherent information
content of the data, but it increases the dynamic range of the
chosen features so that they can be detected easily.

4. 4
Introduction (Contd.)
The existing techniques of image enhancement can be classified into
two categories:
• Spatial domain enhancement
• Frequency domain enhancement
Fig 1. Examples of Image Enhancement Techniques. a) Noise Removal b) Contrast adjustment

5. 5
Techniques used (Point Operation)
• Operation deals with pixel intensity values individually.
• The intensity values are altered using particular transformation techniques as
per the requirement.
• The transformed output pixel value does not depend on any of the
neighbouring pixel values of the input image
Examples:
Fig 2. a) Image Negative b) Contrast Stretching

6. 6
Contrast Stretching
If T(r) has the form as shown in the figure below, the effect of applying the
transformation to every pixel of f to generate the corresponding pixels in g
would:
Produce higher contrast than the original image, by:
– Darkening the levels below m in the original image
– Brightening the levels above m in the original image
• So, Contrast Stretching: is a simple image enhancement technique that
improves the contrast in an image by 'stretching' the range of intensity
values it contains to span a desired range of values. Typically, it uses a
linear function

7. 7
Contrast Stretching (contd.)
• Aims to increase (expand) the dynamic range of an image. It
transforms the grey levels in the range {0,1,..., L-1} by a
piecewise linear function.
• The figure below shows a typical transformation used for
contrast stretching.
• The locations of points (r1, s1) and (r2, s2) control the shape of
the transformation function.
Fig 3. Contrast streching

8. 8
Contrast Stretching(contd.)
Fig 4. Contrast stretching. (a) Original image. (b)
Histogram of (a). (c) Result of contrast stretching.
(d) Histogram of (c).
Fig 5. (a) Low-contrast image. (b) Histogram of (a). (c)
High-contrast image resulted from applying full contrast-
stretching in Figure 5 on (a). (d) Histogram of (c)

9. 9
Gray Level Slicing
• Gray-level slicing aims to highlight a specific range [A...B] of gray levels.
It simply maps all gray levels in the chosen range to a high value.
• Other gray levels are either mapped to a low value (Figure 6(a)) or left
unchanged (Figure 6(b)).
• Gray-level slicing is used for enhancing features such as masses of
water in satellite imagery. Thus it is useful for feature extraction.
Figure 6. Gray-level slicing

10. 10
Gray Level Slicing
• Gray-level slicing aims to highlight a specific range [A...B] of gray levels.
It simply maps all gray levels in the chosen range to a high value.
• Other gray levels are either mapped to a low value (Figure 6(a)) or left
unchanged (Figure 6(b)).
• Gray-level slicing is used for enhancing features such as masses of
water in satellite imagery. Thus it is useful for feature extraction.
Figure 6. Gray-level slicing

11. 11
Gray level slicing (contd.)
Fig 7. Example of gray level slicing

12. 12
Histogram Equalization
Histogram Equalization is an automatic enhancement technique which
produces an output (enhanced) image that has a near uniformly distributed
histogram.
For continuous functions, the intensity (gray level) in an image may be
viewed as a random variable with its probability density function (PDF).
The PDF at a gray level r represents the expected proportion (likelihood) of
occurrence of gray level r in the image. A transformation function has the
form
where w is a variable of integration. The right side of this equation is called
the cumulative distribution function (CDF) of random variable r.

13. 13
Histogram Equalization (contd.)
• For discrete gray-level values, we deal with probabilities (histogram
values) and summations instead of probability density functions and
integrals.
• The right side of this equation is known as the cumulative histogram for
the input image. This transformation is called histogram equalization.
• It spreads the histogram of the input image so that the gray levels of the
equalized (enhanced) image span a wider range of the gray scale. The
net result is contrast enhancement.

14. 14
Histogram Equalization (contd.)
• For discrete gray-level values, we deal with probabilities (histogram
values) and summations instead of probability density functions and
integrals.
• The right side of this equation is known as the cumulative histogram for
the input image. This transformation is called histogram equalization.
• It spreads the histogram of the input image so that the gray levels of the
equalized (enhanced) image span a wider range of the gray scale. The
net result is contrast enhancement.

15. 15
Histogram Equalization (contd.)
Table 1. Gray level intensity

16. 16
Histogram Equalization (contd.)
(a) (b)
(c) (d)
Table 2. Worked out example for table 1

17. 17
Advantages
1.Improved visibility
2.Better image quality
3.Increased contrast
4.Reduced noise
5.Enhanced details
6.Increased diagnostic value
7.Increased accuracy

18. 18
Disadvantages
1.Over-enhancement
2.Artificial look
3.Loss of information
4.Technical expertise
5.Cost
6.Time-consuming
7.Interpreting results
8.Bias

19. 19
Applications
• In atmospheric sciences, image enhancement is used to reduce the
effects of haze, fog, and turbulent weather for meteorological
observations
• In forensics, Image enhancement is used for identification, evidence
gathering and surveillance.
• Astrophotography faces challenges due to light and noise pollution that
can be minimized by IE
Figure 8. a) Forensic application b)Astrophotography application

20. 20
Applications (Contd.)
• In oceanography, the study of images reveals interesting features of
water flow, sediment concentration, geomorphology and bathymetric
patterns to name a few.
• Numerous other fields including law enforcement, microbiology,
biomedicine, bacteriology, etc., benefit from various IE techniques.
Figure 8. a) Applications in oceanography b)applications in medical imaging

21. 21
Summary
•Image enhancement is the process of improving the quality, clarity, and visual
appeal of an image.
•Image enhancement techniques can help improve the visibility of details in an
image, increase contrast, reduce noise and artifacts, and reveal hidden details.
•There are various image enhancement techniques, including histogram equalization,
filtering, thresholding, and color correction.
•Image enhancement techniques can have several applications, including scientific
research, medical diagnosis, industrial inspection, and entertainment.
•Image enhancement techniques can be performed using software tools and
algorithms, as well as hardware devices such as cameras and scanners.
•While image enhancement can provide significant benefits, it is important to apply
these techniques carefully and avoid over-enhancement, which can result in loss of
information and unnatural image appearance.

22. INDIAN INSTITUTE OF TECHNOLOGY ROORKEE
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