Image restoration under significant additive noise

The task of deblurring, a form of image restoration, is to recover an image from its blurred version. Whereas most existing methods assume a small amount of additive noise, image restoration under significant additive noise remains an interesting research problem. We describe two techniques to improve the noise handling characteristics of a recently proposed variational framework for semi-blind image deblurring that is based on joint segmentation and deblurring. One technique uses a structure tensor as a robust edge-indicating function. The other uses nonlocal image averaging to suppress noise. We report promising results with these techniques for the case of a known blur kernel.