Image Enhancement using Histogram Equalization and Histogram Matching
This assignment explores two common techniques for image enhancement: histogram
equalization and histogram matching. You will implement these techniques in Python
and analyze their effect on image quality.
Objectives:
Implement these techniques in Python using OpenCV or Scikit-image libraries.
Analyze the visual changes in images after applying these techniques.
Task 1: Histogram Equalization
1. Load an Image: Load a grayscale image of your choice. (e.g., using
OpenCV cv2.imread() function)
2. Histogram Analysis:
o Calculate (cv2.calcHist) and visualize (matplotlib.pyplot
plot) the histogram of the original image.
o Explain what the histogram reveals about the image's contrast and
intensity distribution.
3. Implement Histogram Equalization:
o Implement histogram equalization using OpenCV
(cv2.equalizeHist()) function.
o Apply the equalization to your image and visualize the resulting image and
its histogram.
o Explain how histogram equalization affects the image's visual appearance
and contrast.
Task 2: Histogram Matching
1. Load Images: Load two grayscale images of your choice (e.g., using
OpenCV cv2.imread() function): target (original) and reference image.
2. Apply histogram matching (explore match_histograms method from skimage
library) to the target image using the reference histogram.
3. Display the original image, reference image, and matched image for
comparison.
4. Visualize the histogram of all three images and explain what the histogram
reveals about the image's contrast and intensity distribution.
Deliverables:
Python code implementing histogram equalization.
A report including:
o Images showcasing the original, histogram-equalized and histogram
matched versions.
o Visualizations of the corresponding histograms for each image.
o Discussion of the observed changes in image contrast and intensity
distribution.
Submission
You should submit (using Blackboard link) the source file which includes the code
(Jupiter notebook) and the report.
This assignment explores two common techniques for image enhancement: histogram
equalization and histogram matching. You will implement these techniques in Python
and analyze their effect on image quality.
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