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

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

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Duke University

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

496 notes

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.

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

Rencontrer les enseignants

Guillermo Sapiro
Professor
Electrical and Computer Engineering

No and welcome back. Let us see the wiener filter in action

once again using MATHLAB. As we have done before we start by

loading an image. This is the same image we have seen

before, this Saturn image, remember that was a color image.

So we're going to transform it into black and white image, the same operation as we

did before. Then we're going to add Gaussian noise,

as we see here, we have seen this before when we were showing the different types

of noise and we see here the variance. Now, are go and apply the wiener filter.

This is the operation that applies the wiener filter to this image that we have

just basically created by adding Gaussian noise.

Now before I show you the result of the Wiener filter I want to compare that with

a different filter. Basically, I want to compare it with a

local averaging. For that I'm creating a disk of radius

ten and then basically filtering the noise image with that disk.

This is basically, this operation is basically computing the

local average, weka window of radius ten.

Don't worry about this, this is just a, what, is telling MATHLAB

what to do at the boundary conditions. So basically, we're going to have, the

original image, we're going to have the noisy image, we're going to have the

blurry image, local averaging, and we're going to have the result of Wiener

filtering. So let's see the results.

Once again we're loading all the images. And this will help us to see how nice

wiener filtering can work. we see again the original image, that we

have seen in the past. This is the noisy image.

This is the result of Wiener filtering. And this is the result of blurring, or

local averaging. So see how nicely Wiener filtering has

been able to recover. Restore a lot of the original image,

starting from this noisy image. And it has done a much better job than

the local averaging. Let me move the image closer, and we can

see the difference. This is clearly a much sharper image than

what we obtained knowing basically, doing the local averaging.

So wiener as we see can do a fantastic job when we provide some information.

And in this case, actually, the wiener is estimating the information.

So this is the, basically the implementation that we saw

at the end of the previous video, the simplest implementation of the wiener

filtering and still able to do a great job in restoring the image, starting from

this noisy image. Then we once again, this was the

original, the noisy of course the wiener filter doesn't know this original images

starts from this noisy image and still operating that blurry version of it,

creates a really sharp version. So it's doing a really, really good job.

And we can see much better than the local averaging that blurs.

So by this we conclude that themma on the wiener victory.

Thank you very much.

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