Okay, so welcome back everybody to interventional medical image processing.

Everything okay?

Yes.

Okay, good.

So welcome.

Wow, you're going so far back.

So there's competition.

Okay, so today we will talk about the last chapter in pre-processing and what we are

going to look at today is image super resolution.

So it says super resolution image reconstruction and you will find that the way of processing

super resolution images is very similar to a reconstruction process.

So we will first discuss what is super resolution, what is the idea of super resolution, then

we will talk a bit about cameras and sampling and what actually happens when you discretize

an image and this is followed by single and multi-camera super resolution.

So there's two different kinds of methods.

So you can do super resolution with a single frame using prior knowledge or you use multi-frame

super resolution where you have a sequence of frames and you use an estimate of the motion

in between to compute super resolved images.

Based on that we will then look into like statistically based super resolution.

So the idea is that there is also a relation to the image statistics, to the noise statistics

that you can build in order to design super resolution approaches and we will finish by

take home messages.

Okay, so what is super resolution?

So you may know CSI Miami or one of these nice television series and there they do super

resolution all the time.

So they take a video and then they zoom in into the mirror of a car and then there is

a reflection and you zoom further in and this is basically the idea of super resolution

and you will see today that if you actually try to do that it's actually much harder than

in the videos.

So we won't be zooming into images and looking into reflections and then recognize faces

and reconstruct them so we won't be able to do that.

But it's still a fancy technique so it's still very nice and we will have a couple of examples

of different modalities where we apply super resolution.

One nice thing is you can use for example multi-frame or multiple sensors and in this

way you can get quite high resolution images from a pretty cheap sensor.

So this is one application where super resolution became popular in the last few years because

you can use it in a very cheap sensor technology.

The downside is of course the money you save on your sensor you have to spend on computation

time.

So you probably can use very cheap sensors and then you ship a cluster with it in order

to compute the super resolved images.

Now you can do that for example on graphics hardware very efficiently but there is a considerable

amount of computing required.

So let's go to the problem description.

A real imaging system performs a non-ideal mapping of a scene to some kind of image plane.

This is the image plane of your camera and it does a non-ideal mapping.

And there's a couple of steps happening in between.

So if you consider your image plane and your sampling there first of all you have to sample

or essentially down sample.

So the signal that you get actually has if you consider it as a continuous signal it