Arithmetic Operations on Images using OpenCV
Last Updated :
07 Aug, 2025
Arithmetic operations such as addition, subtraction and bitwise operations (AND or, NOT, XOR) are fundamental techniques in image processing with OpenCV. These operations allow for the enhancement, analysis and transformation of image characteristics, making them essential for tasks like image clarification, thresholding, dilation and more.
Step-by-Step Implementation
Let's see the step by step implementation of Arithmetic operations,
Step 1: Install Required Libraries and Import necessary Packages
opencv-python (cv2): Core library for image processing and computer vision.matplotlib.pyplot: For displaying images inside the notebook .numpy: Efficient array operations .
Python
!pip install opencv - python matplotlib
import cv2
import numpy as np
import matplotlib.pyplot as plt
from google.colab import files
The samples used can be downloaded from here.
- files.upload() opens a dialog to pick files from our device.
- cv2.imread() reads an image from disk and loads it as a NumPy array (in BGR color ordering by default).
Python
img1 = cv2.imread('input1.png')
img2 = cv2.imread('input2.png')
if img1.shape != img2.shape:
img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
line_thickness = 5
height = img1.shape[0]
line = np.full((height, line_thickness, 3), (0, 0, 255), dtype=np.uint8)
side_by_side = np.hstack((img1, line, img2))
side_by_side_rgb = cv2.cvtColor(side_by_side, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(12, 6))
plt.imshow(side_by_side_rgb)
plt.title('input1 input2')
plt.axis('off')
plt.show()
Output:
1. Image Addition
1.1 Simple Addition
cv2.add(): Adds pixel values with saturation.
Python
added = cv2.add(img1, img2)
added_rgb = cv2.cvtColor(added, cv2.COLOR_BGR2RGB)
plt.imshow(added_rgb)
plt.title('Addition (cv2.add)')
plt.axis('off')
plt.show()
Output:
Simple Addition1.2 Weighted Addition
cv2.addWeighted(): Blends two images by specified weights and an optional scalar.
Parameters:
- img1, img2: input images
- 0.7, 0.3: weights (how much each image contributes)
- 0: gamma (brightness adjustment)
Python
weighted = cv2.addWeighted(img1, 0.7, img2, 0.3, 0)
weighted_rgb = cv2.cvtColor(weighted, cv2.COLOR_BGR2RGB)
plt.imshow(weighted_rgb)
plt.title('Weighted Addition (cv2.addWeighted)')
plt.axis('off')
plt.show()
Output:
Weighted Addition2. Image Subtraction
- cv2.subtract(): Subtracts each pixel in img2 from img1 (clips negative values to 0).
- Used for change detection, background subtraction, etc.
Python
subtracted = cv2.subtract(img1, img2)
subtracted_rgb = cv2.cvtColor(subtracted, cv2.COLOR_BGR2RGB)
plt.imshow(subtracted_rgb)
plt.title('Subtraction (cv2.subtract)')
plt.axis('off')
plt.show()
Output:
Subtraction3. Bitwise Operations
3.1 Bitwise AND
cv2.bitwise_and(): Only keeps pixels where both images have bits "on".
Python
and_img = cv2.bitwise_and(img1, img2)
and_img_rgb = cv2.cvtColor(and_img, cv2.COLOR_BGR2RGB)
plt.imshow(and_img_rgb)
plt.title('Bitwise AND')
plt.axis('off')
plt.show()
Output:
Bitwise AND3.2 Bitwise OR
cv2.bitwise_or(): Keeps pixels if either image has a bit "on".
Python
or_img = cv2.bitwise_or(img1, img2)
or_img_rgb = cv2.cvtColor(or_img, cv2.COLOR_BGR2RGB)
plt.imshow(or_img_rgb)
plt.title('Bitwise OR')
plt.axis('off')
plt.show()
Output:
Bitwise OR3.3 Bitwise XOR
cv2.bitwise_xor(): Keeps pixels if only one image (not both) has a bit "on".
Python
xor_img = cv2.bitwise_xor(img1, img2)
xor_img_rgb = cv2.cvtColor(xor_img, cv2.COLOR_BGR2RGB)
plt.imshow(xor_img_rgb)
plt.title('Bitwise XOR')
plt.axis('off')
plt.show()
Output:
Bitwise XOR3.4 Bitwise NOT
cv2.bitwise_xor(): Keeps pixels if only one image (not both) has a bit "on".
Python
not_img = cv2.bitwise_not(img1)
not_img_rgb = cv2.cvtColor(not_img, cv2.COLOR_BGR2RGB)
plt.imshow(not_img_rgb)
plt.title('Bitwise NOT (Image 1)')
plt.axis('off')
plt.show()
Output:
Bitwise NOT
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