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I suggest you to investigate Image Restoration using deconvolution since it is more appropriate for your application. Kalman filtering is designed to remove or ignore noisy data in a signal but it is not suited for deblurring (as far as I know).

Noting $\epsilon$ the additive noise, $H$ the convolutional mask (your Point Spreading Function), $x$ the original image you want to retrieve and $y$ the blurred image you have:

$y = H*x + \epsilon$

Deconvolution allows you to retrieve $x$ knowing $y$ and $H$.

Using Matlab you can use the following function:

deconvreg(image, PSF)

where PSF is an estimate of the Point Spread filter and image is the blurred and noisy image.

If you want to know more about the theory, this is called an ill-posed problem that you solve by minimizing a Least Square Error through an iterative process:

$$x_{estimated} = \arg\min_{x}||y - Hx||^2$$

Under some approximations this can be computed in a fast way through FFT.

This does not answer your initial question about Kalman Filtering but I hope it will help you! If you are interested I have documents about image restoration using inverse and Wiener filtering.