Blind Image Deblurring by Spectral Properties of Convolution Operators
This addresses the problem of recovering sharp images from blurry ones without known kernels for computer vision applications, representing a novel method for a known bottleneck.
The paper tackles blind image deblurring by analyzing spectral properties of convolution operators to derive an image-dependent regularizer for the blur kernel, showing that this approach significantly improves deblurring results in simulations and real images.
In this paper, we study the problem of recovering a sharp version of a given blurry image when the blur kernel is unknown. Previous methods often introduce an image-independent regularizer (such as Gaussian or sparse priors) on the desired blur kernel. We shall show that the blurry image itself encodes rich information about the blur kernel. Such information can be found through analyzing and comparing how the spectrum of an image as a convolution operator changes before and after blurring. Our analysis leads to an effective convex regularizer on the blur kernel which depends only on the given blurry image. We show that the minimizer of this regularizer guarantees to give good approximation to the blur kernel if the original image is sharp enough. By combining this powerful regularizer with conventional image deblurring techniques, we show how we could significantly improve the deblurring results through simulations and experiments on real images. In addition, our analysis and experiments help explaining a widely accepted doctrine; that is, the edges are good features for deblurring.