Image deblurring (by Marina)

First, I want to apologize for our (little bit longer) disappearance from the blog. I hope that with next several posts you will see the reason behind it.
Here, I want to start with an introduction of my specific topic, blind image deblurring. After the introduction, following posts will be about some of my recent publications, a little bit more technical, but still, I hope, interesting.

What is image deblurring?
It is a process where you have an observed blurred image and you want to estimate, to find, a sharp original image hidden “behind” the blurred image (figure 1).

Figure 1: Image deblurring.

The blurring of the image can have different reasons, like camera shake or out-of-focus, and it is usually described with the point spread function (PSF) or a blurring operator (filter). As there are different reasons for blurry image, there are different blurring filters (figure 2).

Figure 2: Blurring filters: Gaussian, linear motion, out-of-focus, uniform and nonlinear motion blur.

There are two types of image deblurring:
1.    Non-blind image deblurring – where we know how the blurring filter looks like.
2.    Blind image deblurring – when we have a partial knowledge or no knowledge at all about the blurring filter.

The second problem is more realistic one (because usually, we do not know how the blurring occurs) and it is the one that I am interested in. Also, blind image deblurring is more complicated to solve, because in this case we only have a blurred image (one that we observed), and we need to find an underlying sharp image and a blurring filter at the same time. Also, an additional problem that we have is a presence of noise, like we explained in one of the previous posts, that makes our job even harder.