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

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

Duke University

4.7 (689 ratings) | 34K Students Enrolled

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.

Skills You'll Learn

Image Processing, Image Compression, Image Restoration, Image Segmentation

Reviews

4.7 (689 ratings)

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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.

From the lesson

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

Taught By

Guillermo Sapiro
Professor

Transcript

Hello and welcome back. Now that we know, the underlying framework of non-vocal means, let's just demo it in real time. Non-local means denoising works using the resources in the image processing online journal. This is a website that I have provided a link in their webpage for this class, is a journal that has a lot of online algorithms for image processing. And I strongly suggest that you visit it if you want to play with a lot of the state of the art in image processing. So the journal has first a full description of the algorithm. It has also code that you can download and use in your own computer, but also has an online demo and that's what we are going to do next. You can upload your own images, or you can basically use the images already available on the website, just to illustrate and to help you understand the algorithm better. So let's just pick this image, for example. And it's here. And we're going to run the algorithm. We're going to add some noise to the image to see how non-local means will de-noise that, and we're going to ask you to run. So, now it's running, we see this. This algorithm can be made to run very fast, but now it's running on a server and it's running on an implementation that is not optimized for that. But here we have the result. So we see, let's just illustrate the details. We see here is the noisy image. Basically, the original image with the other noise, and now we see the de-noised image. Again, this is the noisy image, this is the de-noised image, and this is the original. So we see that there are differences, but certainly the de-noised image is much better than the noisy image. Now, we can actually see the difference between the de-noised image and the original image. Once again, this is the noisy image, the de-noised image, the original image, and the difference. They just run with a different image. So let's just select a new input here, and we can pick a different image, for example, these dice. And we can pick a different level of noise, and just put a lot of noise to see what happens here, and then let's just run it. Once again, while this is running, remember, the goal of non-local means is to look for regions, say, okay, if I want to de-noise this region, I'm going to look for similar regions around and average them. The larger the noise, the more the algorithm will take, because it's goign to try to average more and more patches to help us reduce the noise, the noise even further. So here is, again, the noisy image the algorithm has finished. This is the noisy. This is the de-noised image. This is the original, and this is the difference. So we think it has done a very, very good job in de-noising this image. Noisy? De-noise. Noisy, de-noise. Original image and the difference. This image doesn't have a lot of texture. And that's why these type of algorithms work really, really, well. There, when there is a lot of texture, the algorithm still works very well, but for every denoise image denoising algorithm, dealing with texture is a bit more difficult. So let's just conclude this by showing this once again. Noisy, de-noise, original, and difference. And once again, you can just go into the ipo.im website and play with this or any other algorithm that is available there, and you can upload your own images and see how it works. Thank you, and see you in the next video.

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