Space-varying blur kernel estimation and image deblurring

Abstract

In recent years, we have seen highly successful blind image deblurring algorithms that can even handle large motion blurs. Most of these algorithms assume that the entire image is blurred with a single blur kernel. This assumption does not hold if the scene depth is not negligible or when there are multiple objects moving differently in the scene. In this paper, we present a method for space-varying point spread function (PSF) estimation and image deblurring. Regarding the PSF estimation, we do not make any restrictions on the type of blur or how the blur varies spatially. That is, the blur might be, for instance, a large (non-parametric) motion blur in one part of an image and a small defocus blur in another part without any smooth transition. Once the space-varying PSF is estimated, we perform space-varying image deblurring, which produces good results even for regions where it is not clear what the correct PSF is at first. We provide experimental results with real data to demonstrate the effectiveness of our method.