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1. Image Processing Task – Image
Sharpening
PES Institute of Technology &
Management
Department of Electronics and
Communication Engineering

2. Problem Statement
• Objective: Enhance the edges of an image
using a sharpening filter.
• • 1. Load the image.
• • 2. Apply a kernel to sharpen the image (e.g.,
Laplacian or unsharp masking).
• • 3. Save and display the sharpened image.

3. Importance of Image Sharpening
• Improves visibility of image features.
• • Highlights object boundaries and enhances
fine details.
• • Used in applications like medical imaging
and object detection.

4. Techniques for Image Sharpening
• Laplacian Filter: Detects edges by calculating
second derivative.
• • Unsharp Masking: Enhances contrast by
subtracting a blurred version.
• • Kernels highlight rapid intensity changes.

5. Step-by-Step Approach
• 1. Load image using OpenCV or PIL.
• • 2. Apply sharpening kernel (Laplacian or
unsharp).
• • 3. Save and display the sharpened output.

6. Sample Kernel Matrices
• Laplacian Kernel:
• [ 0 -1 0 ]
• [-1 4 -1]
• [ 0 -1 0 ]
• • Unsharp Masking Kernel:
• [-1 -1 -1]
• [-1 9 -1]
• [-1 -1 -1]

7. Output Examples
• Before and After comparison of the image.
• • Visual improvement in edge definition and
clarity.
• • (Insert images here)

8. Conclusion
• Sharpening improves image detail and edge
clarity.
• • Choice of kernel affects sharpness and noise
levels.

9. Bonus Task
• Experiment with various sharpening kernels.
• • Analyze effects on different types of images.
• • Observe how sharpness and clarity vary.

10. References
• OpenCV Documentation
• • Image Processing Books and Lecture Notes
• • Assignment Provided by PESITM

11. Sharpening Image - Python Code
• import cv2
• import numpy as np
• # Load the image
• image = cv2.imread('input.jpg')
• # Define sharpening kernel (Unsharp Masking)
• kernel = np.array([[-1, -1, -1],