Computer Vision
Lab 02: Intensity Transformations
Lab Objectives:
In this lab, you will:
 Histogram of image.
 Histogram Equalization.
1.1
Image Negative:
In digital image processing, the negative of an image is an operation that involves
inverting the colors of the image. It is a simple image processing operation that can be
used to enhance the visibility of certain features in an image or to produce interesting
visual effects.
The process of obtaining the negative of an image involves subtracting the pixel values
of the original image from the maximum pixel value that can be represented by the
image format. For example, if the image format uses 8 bits per pixel, the maximum
pixel value is 255. To obtain the negative of an image, we subtract each pixel value
from 255.
The resulting image will have colors that are the complement of the original colors.
For example, black pixels in the original image will become white pixels in the negative
image, and vice versa. Similarly, red pixels in the original image will become cyan pixels
in the negative image, green pixels will become magenta, and blue pixels will become
yellow.
The negative of an image can be useful in a variety of applications, including medical
imaging, microscopy, and computer vision. For example, in medical imaging, the
negative of an X-ray image can be used to enhance the visibility of certain features,
such as bone fractures. In microscopy, the negative of an image can be used to
highlight the shape and structure of cells or other microscopic objects. In computer
vision, the negative of an image can be used as a preprocessing step for edge detection
or other image analysis tasks.
Negative of image equation.
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Computer Vision
Lab 02: Intensity Transformations
1.2
Log Transformation:
In digital image processing, log transformation is a common technique used to
enhance the contrast of an image. The transformation is achieved by taking the
logarithm of each pixel value in the image.
The formula for log transformation is as follows:
s = c * log(1 + r)
where s is the output pixel value, r is the input pixel value, c is a constant scaling factor,
and log is the natural logarithm.
The log transformation has the effect of compressing the dynamic range of the image.
In other words, it enhances the contrast of the image by increasing the brightness of
dark regions and decreasing the brightness of bright regions. This can be particularly
Copyright © [2023] [Hamid Ashfaq]. All rights reserved.
Computer Vision
Lab 02: Intensity Transformations
useful in images that have a large range of brightness values, such as medical images
or satellite images.
One of the advantages of log transformation is that it is a nonlinear transformation,
meaning that it can enhance contrast in a way that is not possible with linear
transformations such as histogram equalization. However, log transformation should
be used with caution, as it can introduce artifacts in some images and can also cause
loss of information in very dark or very bright regions.
Log transformation is a common technique used in a variety of image processing
applications, including medical imaging, remote sensing, and computer vision. It can
be implemented using standard image processing software or programming
languages such as MATLAB or Python.
The above picture shows the importance of histogram in Image Processing domain.
1.3
Histogram of Image?
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Computer Vision
Lab 02: Intensity Transformations
In digital image processing, a histogram is a graphical representation of the distribution of
pixel intensity values in an image. The histogram of an image can provide useful information
about the contrast and brightness of the image, as well as the overall distribution of pixel
intensities.
A histogram is created by counting the number of pixels in an image that have a specific
intensity value, and then plotting these values as a bar graph. The horizontal axis of the
histogram represents the range of possible intensity values, while the vertical axis
represents the number of pixels in the image that have each intensity value.
For example, in a grayscale image with 256 possible intensity values (ranging from 0 to 255),
the histogram would have 256 bins, one for each possible intensity value. The height of each
bar in the histogram would represent the number of pixels in the image that have that
intensity value.
The shape of the histogram can provide valuable insights into the properties of the image. A
histogram with a peak at the centre of the intensity range indicates an image with average
brightness, while a histogram with a peak at either end of the intensity range indicates an
image with low or high contrast. A histogram with multiple peaks may indicate an image
with multiple dominant colours or textures.
Histograms can be used for a variety of image processing tasks, including contrast
stretching, histogram equalization, and thresholding. These techniques can be used to
enhance the contrast and visibility of certain features in an image, or to segment an image
into regions based on intensity values. Histograms can also be used to compare the
properties of different images or to identify anomalies in an image dataset.
Copyright © [2023] [Hamid Ashfaq]. All rights reserved.
Computer Vision
Lab 02: Intensity Transformations
1.4
Histogram Equalization of Image
Histogram equalization is a technique used in digital image processing to enhance the
contrast and brightness of an image. The technique works by adjusting the image's
histogram so that the distribution of pixel intensities is more uniform across the entire
range of possible intensity values.
The process of histogram equalization involves the following steps:
Compute the image histogram: Count the number of pixels in the image that have each
possible intensity value.
Compute the cumulative distribution function (CDF) of the histogram: Calculate the
cumulative sum of the histogram values, normalized by the total number of pixels in the
image.
Compute the desired intensity transformation function: Apply a suitable transformation
function to the CDF to obtain a new intensity mapping that will redistribute the pixel
intensities in the image.
Apply the transformation function to the image: Map each pixel in the image to its new
intensity value according to the desired transformation function.
The result of histogram equalization is an image with improved contrast and brightness,
where the full range of possible intensity values is utilized more evenly. The technique is
particularly effective for images with low contrast or with a narrow range of intensity
values, such as images captured under poor lighting conditions.
Histogram equalization can be implemented using standard image processing software or
programming languages such as MATLAB or Python. However, it should be used with
caution, as it can sometimes result in artifacts or distortions in the image, particularly in
regions with very high or very low pixel intensities. Variations of histogram equalization,
Copyright © [2023] [Hamid Ashfaq]. All rights reserved.
Computer Vision
Lab 02: Intensity Transformations
such as adaptive histogram equalization, may be used to overcome some of these
limitations.
Copyright © [2023] [Hamid Ashfaq]. All rights reserved.
Computer Vision
Lab 02: Intensity Transformations
Lab Task:
Note:
 Built in Libraries are not allowed to use. Create your own code. Marks will be
deducted. If used libraries except basic libraries to read, write and display functions.
 Use of at least one grayscale image and one coloured image RGB in each question.
Questions:
1. Create Negative of image in Python. Use of grayscale image and one coloured image
RGB.
2. Write a code for log transformation in python.
Copyright © [2023] [Hamid Ashfaq]. All rights reserved.
Computer Vision
Lab 02: Intensity Transformations
3. Write a code to show histogram of image.
4. Enhance the image using histogram equalization.
Copyright © [2023] [Hamid Ashfaq]. All rights reserved.