Hello, and welcome back. We are going to now give an example of Calculus of variations used in image processing, and these are the techniques we just saw in the previous video. Let's apply them to image processing now. And we're going to do that with one of the most famous equations in image processing, in the area of partial differential equations in image processing, and that's Anisotropic Diffusion. What is that? If you remember, when we discussed the Gaussian filtering and averaging images, we talked that those are, were like diffusion of the pixel values all across the image. What was happening is that we obtained blurring. Because if there were edges basically, we were averaging across edges. We were letting the pixels' values across edges to be basically mixed up and that's when we obtained blurry. And that was Isotropic Smoothing. It was just going all around. It didn't matter if it had boundaries or not boundaries. What Anisotropic Smoothing or Anisotropic Diffusion is trying to do is going to try to average pixels value, pixel values, only on the right side of the object, on the right side of the edge, on the correct object. So, pixel values here are going to be averaged among themselves, to basically denoise or enhance the image. Pixel values here are going to be basically mixed among themselves. How do we do that? We do that with equations, partial differential equations as we see here. Before we look at the equations, let us look at the images, this is an original image. And if we do basically, Gaussian Smoothing or Averaging, we know that we are going to blur. We blur across the boundaries because it's just mixing pixels from different objects and that's what we see here. On the other hand, if we try not to blur, try not to mix, we only mix pixels on the same side of the boundary without going in this direction, then we get the much sharper. We still see that we have removed noise inside the object, inside the grey matter of these MRI picture of the brain, we see that this is much smoother, that we are still preserving the boundaries very, very, nicely. The difference between these two is here, we have what we have marked here with red background, is isotropic diffusion, heat equation. We actually already talked about it that we're going to to derive it in the next slide using Calculus of variations. Here, we have added isotropic before we take the second derivative which is a divergence. I have to basically remind you that the Laplacian of the image is the divergence of the gradient. And we are ready to define gradient and the divergence in the previous slides. So, before we take it as here, we introduce a function in between here. This is a very similar function as the one that we used for active counters. It's, for example, one over the gradient, we are going to see that in the next slide. So, a function is going to say, wait a second. Don't diffuse if there is a strong gradient. Stop the diffusion. And that's why there is diffusion in certain directions when there is not a strong gradient. And there is no diffusion or reduced diffusion where there is a strong gradient, meaning across edges. And that's where we get very sharp boundaries. We preserve them while at the same time, regularizing inside the objects. And that's what we want. These equations are the results of Calculus of variations. So, if we basically define any function of the gradient, so here, this is what we saw the last time that we were basically taking functions of u or derivatives of u. And basically, the gradient is the derivative of the image and rho here takes the role of f. So, we take any function of the magnitude of the gradient. If we compute the Euler-Lagrange, this is what we get. That's the Euler-Lagrange of that equation if we follow exactly the formula that we show in the previous video. And remember, the partial differential equation is obtained by deforming the image equal to the Euler-Lagrange. And then, when this doesn't change anymore, we have solved the Euler-Lagrange and we have obtained may be only local, but at least we obtained that minimizer of this functional. So, once again, this is just by doing the Euler-Lagrange that we learned in the previous video. We just pick a couple of examples of this raw function to show how nice this is. For example, let us consider rho of a to be a squared. So here, I am trying to minimize the magnitude of the gradient squared. And then, the Euler-Lagrange, so ir rho of a is a squared, then rho prime is 2a, okay? So, instead of rho prime, I have to put two times whatever is inside rho, which is the gradient, okay? So basically, have here, and then the Euler-Lagrange that I get is, the Euler-Lagrange equation is pie t, this is this, equal to divergence of rho prime, basically 2a. Let me ignore the two, it's just scale. So, a time, we mean, replace the gradient, okay? a is taking basically the position of the gradient, the absolute magnitude of the gradient, sorry. And this is this part. So, this part stays and this part is rho prime. Now, these two cancel and I go divergence of basically the gradient and this is the Laplacian. For this function, the Euler-Lagrange is basically the heat flow and that's isotropic. That's basically smoothing it in an isotropic fashion, okay? So, that's the Euler-Lagrange of the square of the absolute value of the magnitude, we get the regular heat flow, the Isotropic Diffusion, diffusion all around. Let's just pick another example that is really, really, really interesting. Let's just pick, for example, a function that basically, rho of a = a, okay? Just itself, we could take the absolute value of a but let's just leave that technicality aside for a second. Then, rho prime is one, okay? So, I'm basically going to try to minimize the absolute value of the gradient, not the gradient square, the absolute value of the gradient without putting it to the square. And then, what do I get as the Euler-Lagrange equation of that? So, rho prime is now a constant. So, I go, divergence of gradient normalized by the gradient. So, I didn't get anymore the heat flow. I got this normalization factor that is saying, wait a second, if the grand is very high, you are one over the gradient so slow down and try to preserve those edges. So, this is one example of an Isotropic Diffusion. It's actually called the total variation. Total [SOUND] variation, that's basically the name of this equation. And now, I want you to think for a second, what is this? We learned it a couple of videos ago. This is curvature of the level lines of the image I. We actually use it as a genetic function. We use it phi, in that case, or, or phi, we use this in that case. Now, this is the curvature of every level line of the image. So, we are basically considering the image as a surface and we are deforming the image, we are doing an Isotropic Diffusion of the image in such a way that basically it's moving according to the curvature of the level lines and that's getting us an Isotropic Diffusion. This is an extremely interesting connection. We talked about curve evolution, we talked about curvature type of motions. This is a curvature type of motion. That basically the image is moving based on curvature of the level lines and that's, we don't really care about the level lines or we didn't derive this equation from the level lines, we derived it from the Euler-Lagrange of the total variation. But we connected between the both, between both of them moving according to curvature type of equations and moving curvature of the level lines and moving according to the Euler-Lagrange. The curvature of what? The curvature of the level lines of the image. A very interesting connection between curve evolution, level set, and Calculus of variation, and that gives us a very, very nice equation for Anisotropic Diffusion. And this basically is one of the most famous examples of the use of Calculus of variations in image processing. What's left in this week is to conclude the week in the next video with a bit more of active contours, one of the main clients of curve evolution and Calculus of variations as we have discussed in the past and that's we're going to see in the next video. Thank you very much. I'm looking forward to that. Thank you.
