Module 3: Image Processing Fundamentals

1. Learning Objectives

• Understand core image processing operations for enhancing image quality
• Apply filters for smoothing, sharpening, and edge detection
• Visualize histograms and apply histogram equalization
• Build practical image processing pipelines using OpenCV

2. Key Concepts

• Point Operations: Adjust brightness and contrast at pixel level
• Linear Filtering: Convolution, smoothing, sharpening
• Edge Detection: Sobel, Prewitt, Canny
• Histogram: Plot of pixel intensity distribution
• Histogram Equalization: Enhances image contrast

3. Common Image Filters

1/9 * [[1,1,1],
        [1,1,1],
        [1,1,1]]

4. Edge Detection Algorithms

• Sobel: First-order derivatives in X or Y
• Prewitt: Similar to Sobel with simpler masks
• Canny: Multi-stage filter: noise reduction → gradient → non-max suppression → hysteresis thresholding

5. Hands-on Examples (OpenCV)

!pip install opencv-python-headless matplotlib

import cv2
import numpy as np
import matplotlib.pyplot as plt
from google.colab import files
from google.colab.patches import cv2_imshow

uploaded = files.upload()
img_path = next(iter(uploaded))
image = cv2.imread(img_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2_imshow(gray)

# 1. Smoothing (Gaussian Blur)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
cv2_imshow(blur)

# 2. Sobel Edge Detection
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
cv2_imshow(cv2.convertScaleAbs(sobelx))
cv2_imshow(cv2.convertScaleAbs(sobely))

# 3. Canny Edge Detection
canny = cv2.Canny(gray, 100, 200)
cv2_imshow(canny)

# 4. Histogram Equalization
equalized = cv2.equalizeHist(gray)
cv2_imshow(equalized)

6. Real-World Example

In industrial inspection, edge detection is used to verify product shape, completeness, or detect defects in manufacturing lines.

7. Assignment