This is your data, this is Y. If I tell you nothing else, but find X

that is close to Y, you're going to stay with that.

But if I tell you. That through this function, if I tell you

that the solution is a straight line, I force you to make a straight line.

Then, you're going to find a solution which is something like that.

Not the original points but something like that.

So I gave you prior information. I gave you a constrain that says, find

something that is close but is also a straight line and then you got that

solution. So the basic idea is that we have two

terms. One that relates dissolution to the

measurements. And one that basically gives you a prior,

a regularization at condition. So it's kind of we're projecting the

observation, Y, into this prior. Now, this is what's called a Bayesian

point of view, following Thomas Bayes, and the basic idea is that we're

computing what's called the Maximum-A-posteriori or the map.

We're basically going to compute. The X that maximizes something, or

minimizes this function. Depending if we take the function as

itself, we call it a minimizer. If we look at the probabilistic framework

that this, basically an exponentiation, so we take this to the exponent and this,

also kind of, to the exponent, that would be a maximization.

But don't worry. We do max or we do min, depending on the

sign and depending. We can always do.

Minus and then we get a maximization, so that's why it's called the

Maximum-A-posteriori. We could give to everything that we are

going to be presenting next, a probabilistic framework, a probabilistic

interpretation and then make this even more clear.

But the basic idea is that we have a prior, and in base language this is

called a likelihood. The prior and the likelihood and both can

have a probabilistic interpretation. Now, Once again, this models the noise,

In this case, additive Gaussian. This models the signal.

And a lot of image and in general signal processing has dedicated a lot of effort

into designing what is the best prior? What is the best space to define images?

To define different types of signals. And there has been a lot of work in the

literature of image processing in that. So.

I'm going to just describe a few. People say, a good prior is, give me an

image that doesn't have too much energy. So this is this example.

There is always a parameter here that can scale it up and down.

There were other priors that say: give me an image that is smooth.

For example, we have seen when we talk about in painting, that one way to make

sure of smoothness is roughness. Instead of having low energy, you say, it

has to be very smooth. Which means that, when I compute the

roughness, the energy has to be low. Another prior is to say, that's correct

but we have edges. So I don't want my edges to be smooth.

I want very sharp edges. And then we do an adaptation that says

not every place it has to be smooth. We allow certain jumps.

Another example, is to basically take functions, not just the quadratic

function, but more sophisticated functions of smoothness and that's

related to the topic of robust statistics.

Just another example, we can talk about total variation, we have seen that when

we need initial tropic diffusion. This was one of the examples of initial

tropic diffusion that we took basically the integral over the gradient, not

gradient square, but the gradient and we got initial tropic diffusion.

We can also do wavelengths, and we haven't discussed in this class about

wavelengths, but you probably have heard about wavelengths.

It's a very important topic in image processing.

Of course, in nine weeks, we cannot discuss everything, so we have not

discussed about. Wavelets in particular.

But people have used that. And you can think about basically

multiplying the matrix, multiplying the image by a matrix, and then doing some

penalty on that product. For example, the L1, as we have here, the

absolute value. What we're going to be discussing during

this week is this particular prior that we have here.

And I am going to explain it more in the next few slides and we're even going to

learn more in the next videos. But this it the prior that we're going to

be talking about. And basically, it's written here.

But again, I'm going to explain it next. This has been basically the evolution

and. Basically, is kind of historical

evolution, they'll know exactly about different types of priors.

That people have used, that people have proposed.

Each one has it's advantages and disadvantages.

We are going to discuss this one. Now there's much more sophisticted priors

that people have proposed in the literature.

Some of those are very good but are computationally extremely expensive and

extremely difficult. One of the features of many of these

priors and as we're going to see also of this one its computations are visible.

We can do them. We can understand the prior and we can do

the computations. So let us explain what is this.

Prior of Sparse Modeling.