12 - Demo - Unsharp masking - Duration 03:10

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Del curso dictado por Duke University

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

352 calificaciones

Duke University

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

352 calificaciones

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.

De la lección

Spatial processing

Some of the most basic tools in image processing, like median filtering and histogram equalization, are still among the most powerful. We will describe these and provide a modern interpretation of these basic tools. Students will then become familiar with simple and still popular approaches. We will also include non-local means, a more modern technique that still uses classical tools.

1 - Introduction to image enhancement - Duration 19:11 - Optional break at 08:3319:11

2 - Demo - Enhancement & Histogram modification - Duration 03:533:53

3 - Histogram equalization - Duration 19:56 - Optional breaks at 04:40 and 11:3019:57

4 - Histogram matching - Duration 08:318:31

5 - Introduction to local neighborhood operations - Duration 06:466:46

6 - Mathematical properties of averaging - Duration 11:0011:00

7 - Non-Local means - Duration 07:277:27

8 - IPOL Demo - Non-Local means - Duration 03:383:38

9 - Median filter - Duration 07:207:20

10 - Demo - Median filter - Duration 01:311:32

11 - Derivatives, Laplacian & Unsharp masking - Duration 14:24 - Optional breaks at 05:21 and 11:3314:24

12 - Demo - Unsharp masking - Duration 03:103:11

13 - Gradients of scalar and vector images - Duration 05:575:57

14 - Concluding remarks - Duration 01:121:12

Conoce a los instructores

Guillermo Sapiro
Professor
Electrical and Computer Engineering

Hello and welcome back. We are going to be using once again,

MATLAB to illustrate what we just learned about answer masking, and a few

additional operations. So, let's go into that.

First we're going to read an image, one of the images that come with the matter

package. Then we're going to add, salt and pepper

noise. We see here point zero nine meaning, that

about 9% of the pixels in the image, are going to be contaminated by salt, white,

or pepper, meaning black noise. After that, we're going to apply to the

noisy image, medium filtering, and we see that operation here.

And then we're going to load, the original image.

The noisy image, the medium filter result, meaning the denoised image. The

difference between these two as one possibility of [INAUDIBLE] masking and

also a stretch histogram. And we're going to see and discuss each

one of these more. So let's just first load all the five

images and see what happens. I'm going to move them around so we can

look at all of them at once. So,

this is once again, we see here first the original image.

Then we see the salt and pepper noise. When the background is white, we can see

the black dots, basically the pepper. When the background is dark, when the

background is dark, we can see the white dots, the salt.

Here we see the result of applying a three by three medium filter to this

image. And we see that we managed to clean the

noise. And we managed to clean pretty much the

whole image. Of course, we have some blurring not a

lot, but some blurring produced by the medium filter.

So it's not exactly the original image. It's kind of a blurred version of it.

Which means that if we take now the difference between these two images,

we're going to get this, which is mostly the edges the basically sharp boundaries

in this image. This image is a bit too dark and

therefore what we do as we know we can stretch the histogram and in this case we

just square the pixel values and by that we are stretching the histogram and we

see here the results. So here is basically, the difference

between the original, the medium filter of the noise filter, that's that and we

have stretching. Now,

observe that we don't see edges, because of the noise.

Those would be superios edges, won't be real edges in the original image.

And that basically happens because we have applied a medium filter before

producing these difference. And that's actually great, because the

medium filter manages to get rid of the noise.

And therefore managed to get rid of the possibility of having superious edges.

So in this example, we see a lot of the things that we have learned.

We see salt and pepper noise, result of medium filter.

The difference producing the most important edges and an histogram

stretching to be able to observe those edges in a much nicer way.

Thank you very much, and see you in the next video.

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