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3 - Demo - Types of noise - Duration 03:03

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Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital

Université Duke

4.7 (690 évaluations) | 34K étudiants inscrits

In this course, you will learn the science behind how digital images and video are made, altered, stored, and used. We will look at the vast world of digital imaging, from how computers and digital cameras form images to how digital special effects are used in Hollywood movies to how the Mars Rover was able to send photographs across millions of miles of space. The course starts by looking at how the human visual system works and then teaches you about the engineering, mathematics, and computer science that makes digital images work. You will learn the basic algorithms used for adjusting images, explore JPEG and MPEG standards for encoding and compressing video images, and go on to learn about image segmentation, noise removal and filtering. Finally, we will end with image processing techniques used in medicine. This course consists of 7 basic modules and 2 bonus (non-graded) modules. There are optional MATLAB exercises; learners will have access to MATLAB Online for the course duration. Each module is independent, so you can follow your interests.

Visualiser le programme de cours

Compétences que vous apprendrez

Image Processing, Image Compression, Image Restoration, Image Segmentation

Avis

4.7 (690 évaluations)

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MK

Oct 14, 2018

A very nice covering of subject from basics to advance level of image processing. A good insight into theory and implementation of the Image processing algorithms.

SM

Oct 12, 2019

I am right now half way through this course. The course is excellent and they way the content is presented by Professor Guillermo Sapiro is really enlightening.

À partir de la leçon

Image restoration

The goal of this unit is to complement Unit 3 by adding prior information about the sources of degradation. Students will learn that if we know about the degradation process, we can do better. The objective of this unit is to complete the training with basic and powerful classical tools.

1 - What is image restoration - Duration 07:497:49

2 - Noise types - Duration 12:4312:43

3 - Demo - Types of noise - Duration 03:033:03

4 - Noise and histograms - Duration 04:524:52

5 - Estimating noise - Duration 10:41 - Optional break at 05:0310:41

6 - Degradation Function - Duration 11:4011:40

7 - Wiener filtering - Duration 12:34 - Optional break at 06:5412:34

8 - Demo - Wiener and Box filters - Duration 03:193:19

9 - Concluding remarks - Duration 00:330:33

Enseigné par

Guillermo Sapiro
Professor

Transcription

Hello, and welcome back. We are going to benefit once again from MATLAB to learn a few of the examples of different types of noise that we have learned in the previous video. Let us, first of all, as we already know how to do that, load an image. This is one other image that comes with a MATLAB packet. This in particular is a color image RGB. So we are going to first transform it into a black and white image. We're basically only going to be paying attention to the luminance component of the image. That's this operation that we see here. Then we're going to add Gaussian noise, that's what we see here. So Gaussian means we are going to add Gaussian noise to this image and that Gaussian noise is going to have a variance of 2. Then we're going to add salt and pepper noise with 2%,. meaning 2% of the pixels in the image I are going to be changed to either black or white, according to the definition of, of salt and pepper noise. And then we are actually going to add 20% of salt and pepper noise. So that's this image. Now, what we are going to do, is we are going to load all the images, the original color image, the black and white version of that image, the Gaussian noise, the 2% salt and pepper noise, and the 20% salt and pepper noise. So let's just see what happens. Once again, we are basically loading all the images. Now, I'm going to move them so we can look at them better, but I think you managed to see some of that already. So there's a lot of images here. And, let's see first the original image. This is the RGB, the color of image that we loaded first. I'm going to remove that so we don't have so many images on the screen. Here is the original image. Here is the result of finding the Gaussian noise. The first observation you can see is basically there is noise all around the image. So, this is Gaussian noise. Every pixel has some noise according to the Gaussian distribution. Contrary to that, we see here that 2% salt and pepper noise. Some of the pixels of the image have been affected, only 2% of them. Now, once they are affected, they become either completely white or completely black as we see that here. Finally, we see what happens when we have 20% of noise. So you see a lot of pixels that basically became either black or white. Once again, compared to only 2%, and compared to the Gaussian noise, that is affecting all the pixels. And finally, then just look again at this nice original image. So these are different types of noise, Gaussian noise and salt and pepper noise, and basically different densities of noise for salt and pepper. We could also have played with different variances for the Gaussian noise, but I think that this is sufficient to illustrate the concept of these types of noise. Thank you very much and see you in the next video.

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