So the problem is given the, low resolution observations,.

K equals 1 to 5 for this particular image, and knowledge

of b of k, s of k, we tried to find an estimate of the high resolution

image, given also maybe some knowledge about the noise.

Now, the motion parameters need to be estimated.

The blur may or may not be known so if it's

not, known then we have a blind, image super resolution problem.

So it's clearly, an inverse problem.

A recovery, problem.

And, any of the approaches we've covered so

far could be potentially applied to solving this problem.

One could follow a sequential approach, according to which first

the motion parameters are estimated, maybe followed by the blur,

and then, after both of them are estimated, they're utilized

here to define, B of k and solve this inverse problem.

>> But, maybe ideally, as was argued at an earlier point,

we would like to estimate all the unknown parameters, along with the

high-res image simultaneously, so that errors that were calved during the

estimation itself, the motion parameter, are,

utilized in estimating the high-res image.

[INAUDIBLE] image.