Image enhancement lecture

Image Enhancement in Digital Image Processing
Israr Hussain
2021/7/05

INSIGHT:
1. Introduction
2. Why Image Enhancement?
3. What is Image Enhancement?
4. Image Enhancement techniques
5. Examples of Image Enhancement techniques
6. Spatial Domain Enhancement
7. Enhancement methods
8. Conversion methods
9. Resources required
10. Experimental results
11. Applications of image enhancement techniques.
12. Conclusion

INTRODUCTION:
An image defined in the “real
world” is considered to be a function of
two real variables, for example, a(x,y)
with a as the amplitude (e.g. brightness) of
the image at the real coordinate position
(x,y)
Image processing is the study of
any algorithm that takes an image as input
and returns an image as output. It includes
the following:
1. Image display and printing
2. Image editing and
manipulation
3. Image enhancement
4. Feature detection
5. Image compression.
JPEG
Original
Compression

WHY IMAGE ENHANCEMENT?
The aim of image enhancement is to improve the visual appearance of an
image, or to provide a “better transform representation for future automated
image processing.
Many images like medical images, satellite images, aerial images and e
v
e
nreal life
photographs suffer from poor contrast and noise.
It is necessary to enhance the contrast and remove the noise to increase i
m
a
g
e
quality.
Enhancement techniques which improves the quality (clarity) of images for
human viewing, removing blurring and noise, increasing contrast, and
revealing details are examples of enhancement operations.

WHAT IS IMAGE ENHANCEMENT?
Image enhancement process consists of a collection of techniques t
h
a
t seek to
improve the visual appearance of an image or to convert the image to a
form better suited for analysis by a human or machine.
The principal objective of image enhancement is to modify attributes o
fan
image to make it more suitable for a given task and a specific observer.
Enhancement
Technique
Input Image “Better” Image
Application specific

IMAGE ENHANCEMENT
TECHNIQUES:
The existing techniques of image enhancement can be classified into
two categories:
• Spatial domain enhancement
• Frequency domain enhancement.

EXAMPLES OF IMAGE ENHANCEMENT
TECHNIQUES:
1. Noise removal
Noisy image De-noised image
2. Contrast adjustment
Low contrast Original contrast High contrast

SPATIAL DOMAIN ENHANCEMENT:
x
•Spatial domain techniques are performed to
the image plane itself and they are based on
direct manipulation of pixels in an image.
• The operation can be formulated as
g(x,y)=T[f(x,y)], where g is the output, f is the
input image and T is an operation on f defined
over some neighbourhood of (x,y).
•According to the operations on the image
pixels, it can be further divided into 2
categories:
oPoint operations and
oSpatial operations (including linear and non-
linear operations).

ENHANCEMENT METHODS:
1.Contrast stretching :
•Low-contrast images can result from poor illumination, lack of dynamic range in
the image sensor, or even wrong setting of a lens aperture.
•The idea behind contrast stretching is to increase the dynamic range of the gray
levels in the image being processed.
• The general form is:
s =
1+ (m / r) E
where, r are the input image values, s are the output image
values, m is the thresholding value and E the slope.
1

Figure shows the effect of the variable E:
• If E = 1 the stretching became a threshold transformation.
• If E > 1 the transformation is defined by the curve which is smoother and
• When E < 1 the transformation makes the negative and also stretching.

2. Noise reduction :
This is accomplished by averaging and median filtering. These
are as follows:
a. Median Filtering :
• The median filter is normally used to reduce noise in an image by
preserving useful detail in the image.
• The median filter considers each pixel in the image in turn and looks at its
nearby neighbors to decide whether or not it is representative of its
surroundings.
• The median is calculated by first sorting all the pixel values from the
surrounding neighborhood into numerical order and then replacing the
pixel being considered with the middle pixel value.

Figure below illustrates an example calculation.
b.Noise removal using Averaging:
• Image averaging works on the assumption that the noise in your image is
truly random.
• This way, random fluctuations above and below actual image data will
gradually even out as one averages more and more images.

Median filtering
This kind of the noise are called salt
and pepper noise
If we apply smooth filtering we cant
remove the noise.
Median filtering steps

Median filtering
Pixel
replication

Pixel Replicated images convol in Median
filtering
Ascending
order

Move the empty mask and note all the 9
pixels

3. Intensity Adjustment :
•
•
•
Intensity adjustment is a technique for mapping an image's intensity values to
a new range.
For example, rice.tif. is a low contrast image. The histogram of rice.tif, shown
in Figure below, indicates that there are no values below 40 or above 225. If
you remap the data values to fill the entire intensity range [0, 255], you can
increase the contrast of the image.
You can do this kind of adjustment with the imadjust function. The general
syntax of imadjust is
J = imadjust(I,[low_in high_in],[low_out high_out])

4. Histogram equalization:
•
•
Histogram Equalization is a technique that generates a gray map which
changes the histogram of an image and redistributing all pixels values to be as
close as possible to a user – specified desired histogram.
It allows for areas of lower local contrast to gain a higher contrast.
Figure above shows the original image and its histogram, and the equalized
versions. Both images are quantized to 64grey levels.

Use of Histogram Equalization
Manipulating Contrast
Brightness
Histogram can control the image Quality
Quality Normalizing Histogram Flat profile
To get a high quality image the histogram should be normalize to be a flat profile

Example
Cumulative Distribution function
Probability distinction fucntion

5. Image thresholding:
•
•
•
Thresholding is the simplest segmentation method.
The pixels are partitioned depending on their intensity value T.
Global thresholding, using an appropriate threshold T:
g(x, y) = 1, if f (x, y) > T
0, if f (x, y) <= T
• Imagine a poker playing robot that needs to visually interpret the cards in its
hand:
Original Image Thresholded Image

If you get the threshold wrong the results can be disastrous:
Threshold Too High Threshold Too Low

6. Grey level slicing
•
•
Grey level slicing is the spatial domain equivalent to band-pass
filtering.
A grey level slicing function can either emphasize a group of intensities
and diminish all others or it can emphasize a group of grey levels and
leave the rest alone.
The figure above shows An example of gray level slicing with and without
background

7. Image rotation:
• Image rotation in the digital domain is a form of re-sampling but is
performed on non-integer points.
•The equation below gives the coordinate transformation in terms of rotation
of the coordinate axis.
Sx = Dx cos(θ) + Dy sin(θ)
Sy = -Dx sin(θ) + Dy cos (θ)
Where, S and D represent source and destination coordinates.
0° rotation 90° rotation 180° rotation

CONVERSION METHODS:
1.Greyscale conversion:
•Conversion of a colour image into a greyscale image inclusive of salient features
is a complicated process.
•The converted greyscale image may lose contrasts, sharpness, shadow, and
structure of the colour image.
•To preserve these salient features, the colour image is converted into greyscale
image using three algorithms as stated:
a. The lightness method averages the most prominent and least prominent
colors: (max(R, G, B) + min(R, G, B)) / 2.
b. The average method simply averages the values: (R + G + B) / 3.
c. The luminosity method is a more sophisticated version of the average
method. The formula for luminosity is 0.21 R + 0.71 G + 0.07 B.

Conversion Method link
https://mmuratarat.github.io/2020-05-
13/rgb_to_grayscale_formulas

The example of sunflower images are as follows:
Original image Lightness
Average Luminosity

Weighted average
This is the grayscale conversion algorithm that OpenCV’s

Average method
Average method is the most simple one. You just have to
take the average of three colors. Since its an RGB image, so
it means that you have add R with G with B and then divide it
by 3 to get your desired grayscale image.
grayscale_average_img = np.mean(fix_img, axis=2)

The luminosity method
This method is a more sophisticated version of the average
method. It also averages the values, but it forms a weighted
average to account for human perception
According to this equation, Red has contribute 21%, Green has
contributed 72% which is greater in all three colors and Blue has
contributed 7%.

2. Image File Format:
•
•
The file format is critical to the preservation of an image.
The TIFF file (tagged image file format) is the current preservation format
because it holds all the preservation information required to create a digital
master of the original.
Some of the file formats are: TIFF Preferred Archival format, JPEG
Irreversible image compression, DNG Universal camera raw format etc.
Original JPEG Compression

RESOURCES REQUIRED:
Software requirements:
1. Windows Operating System XP and above.
2. MATLAB 7.10.0(R2010a)
Hardware requirements:
1. Hard disk: 16GB and above.
2. RAM: 1GB and above.
3. Processor: Dual-core and above.

APPLICATIONS:
Biology Astronomy
Medicines Security, Biometrics

Satellite imagery Personal imagery

• The material presented is representative of spatial domain
technique commonly used in practice for image enhancement.
• This area of image processing is a dynamic field, and new
technique and applications are reported routinely in professional
literature and in new product announcement.
•In addition to enhancement, this serves the purpose of introducing a
number of concepts such as intensity adjustment, contrast stretching,
noise filtering, etc. that will be useful in various fields.
CONCLUSION:

Image enhancement lecture

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