Noise in images appears as unwanted graininess or distortion, often caused by low lighting, compression artifacts, or sensor limitations. Smoothing techniques help **reduce noise while preserving important image details**.

### Gaussian Blurring (Smoothing Noise) ###

The Gaussian filter applies a weighted average to surrounding pixels, giving a natural-looking blur.




### Median Blurring (Salt-and-Pepper Noise Removal) ###

The **median filter** replaces each pixel with the median value of its neighbors. This is effective for removing **salt-and-pepper noise (random white and black pixels)**.


import unittest
import numpy as np
import importlib

class TestFourierTransformFiltering(unittest.TestCase):
    
    @classmethod
    def setUpClass(cls):
        global user_code
        user_code = importlib.import_module("user_code")
    
    def test_gaussian_blur_shape(self):
        """Check if Gaussian blurred image has the correct shape."""
        self.assertEqual(user_code.gaussian_blurred.shape, user_code.image.shape, "Gaussian blurred image should have the same shape as the original image.")
    
    def test_median_blur_shape(self):
        """Check if Median blurred image has the correct shape."""
        self.assertEqual(user_code.median_blurred.shape, user_code.image.shape, "Median blurred image should have the same shape as the original image.")
    
    def test_gaussian_blur_type(self):
        """Ensure Gaussian blur returns a NumPy array."""
        self.assertIsInstance(user_code.gaussian_blurred, np.ndarray, "Gaussian blurred image should be a NumPy array.")
    
    def test_median_blur_type(self):
        """Ensure Median blur returns a NumPy array."""
        self.assertIsInstance(user_code.median_blurred, np.ndarray, "Median blurred image should be a NumPy array.")
    
    def test_gaussian_blur_effect(self):
        """Ensure Gaussian blur changes the image content."""
        self.assertFalse(np.array_equal(user_code.gaussian_blurred, user_code.image), "Gaussian blurred image should be different from the original.")
    
    def test_median_blur_effect(self):
        """Ensure Median blur changes the image content."""
        self.assertFalse(np.array_equal(user_code.median_blurred, user_code.image), "Median blurred image should be different from the original.")
    
if __name__ == "__main__":
    unittest.main()


test_code.py

This course provides a comprehensive introduction to Computer Vision, covering both fundamental and advanced topics. You'll learn how machines process, analyze, and interpret visual data through various techniques such as image processing, feature extraction, object detection, and deep learning models.

Computer Vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer Vision integrates with AI and machine learning. 

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Computer vision has significantly advanced over the years, shifting from basic image processing methods to complex deep learning techniques. This section delves into the latest innovations in computer vision, focusing on transfer learning, facial recognition, and image generation. We will explore the benefits of pre-trained models on performance, the principles of facial recognition technology, and the way AI creates images through deep learning.

Computer Vision Course Outline

Noise Reduction and Smoothing

Gaussian Blurring (Smoothing Noise)

Median Blurring (Salt-and-Pepper Noise Removal)

Solution

Noise Reduction and Smoothing

Gaussian Blurring (Smoothing Noise)

Median Blurring (Salt-and-Pepper Noise Removal)

Solution