Edge-based blur kernel estimation using patch priors

TLDR

Blind image deconvolution, i.e., estimating a blur kernel \(k\) and a latent image \(x\) from a blurred image \(y\), is a severely ill‑posed problem. The paper introduces a new patch‑based strategy for kernel estimation in blind deconvolution. The method estimates a trusted subset of the latent image by applying edge‑ and corner‑oriented patch priors derived from natural image statistics and synthetic structures, then iteratively recovers the partial latent image and blur kernel. Evaluation demonstrates state‑of‑the‑art performance on uniformly blurred images.

Abstract

Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely ill-posed problem. In this paper we introduce a new patch-based strategy for kernel estimation in blind deconvolution. Our approach estimates a "trusted" subset of x by imposing a patch prior specifically tailored towards modeling the appearance of image edge and corner primitives. To choose proper patch priors we examine both statistical priors learned from a natural image dataset and a simple patch prior from synthetic structures. Based on the patch priors, we iteratively recover the partial latent image x and the blur kernel k. A comprehensive evaluation shows that our approach achieves state-of-the-art results for uniformly blurred images.