An Adaptive Patch Prior for Single Image Blind Deblurring

The blind deblurring algorithm aims to restore the blur kernel and sharp image from a degraded image with blurry and noisy artifacts. In this paper, we propose a novel adaptive patch prior model based on local statistics as a constraint term for blur kernel recovery. With this prior, our approach can rebuild the step edge of a patch and enhance low-level features (edges, corners and junctions) by strengthening the guidance to help sharpen edges and texture structures for latent image restoration. Note that our prior is a nonparametric model that does not rely on external statistical image knowledge and only depends on internal patch information for adaptive computation. Moreover, our proposed prior has the ability to alleviate noise and oversharpening artifacts caused by heuristic methods. Experiments on two benchmark datasets and a natural image showed that our approach compares favorably with other state-of-the-art methods for kernel estimation.