Lec 4 Noise Construction Software engineering.pdf

DIP

1. Noise Construction
Instructor: Azka Mir

2. Noise
▪Any unwanted interference that degrades the image.

3. Noise Models

4. Example
Images with and without noise

5. Noise Models

6. Types of Noise
Models

7. Effects of
Different Noise
Models

8. Gaussian Noise
Model

9. Gaussian Noise
Model

10. Impulse Valued/
Salt & Pepper
Noise Model

11. Salt & Pepper
Noise Model

12. When does a
certain noise
occur?

13. MATLAB
IMPLEMENTATION

14. Gaussian
image = double(img);
StandardDeviation = 0.1;
Random_image= StandardDeviation * rand(size(image));
noisy_image = image + random_image;
noisy_image = max(0, min(noisy_image, 1)); % Optional step to
clip the values to the range
imshow(noisy_image);

15. Salt & Pepper
▪image = double(img);
▪probability = 0.05;
▪Random_image= rand(size(image));
▪noisy_image = image;
▪noisy_image(Random_image < prob/2) = 0; % Pepper noise
▪noisy_image(Random_image > 1 - prob/2) = 1; % Salt noise
▪imshow(noisy_image);

16. THE END