Welcome to our tutorials on image processing! Whether you're a beginner or looking to expand your knowledge, these guides will help you understand the basics and advanced concepts of image processing.

Basic Concepts

  • Image Representation: Learn about how images are represented in digital form, including pixel formats and color spaces.
  • Image Filtering: Explore various filtering techniques to enhance or modify images, such as blurring, sharpening, and edge detection.

Advanced Techniques

  • Feature Extraction: Discover methods to extract meaningful features from images, which are essential for tasks like object recognition and classification.
  • Deep Learning in Image Processing: Understand how deep learning models can be used for image processing tasks, such as image classification, segmentation, and generation.

Example: Image Filtering

Here's a simple example of how to apply a blur filter to an image using Python and OpenCV:

import cv2
import numpy as np

# Load an image
image = cv2.imread('example.jpg')

# Apply Gaussian blur
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

# Display the original and blurred images
cv2.imshow('Original Image', image)
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

For more information on image processing with OpenCV, visit our OpenCV tutorials.

Further Reading

Image Filtering Example