Two-stage image deblurring with L0 gradient minimization and non-local refinement
Kai You Wang, Liang Xiao, Zhihui Wei · Pattern Recognition and Image Analysis · 2015
An efficient two-stage non-blind deblurring framework is proposed for recovering blurred images progressively. To this date, most approaches commonly solve a single variational regularization problem incorporated with chosen priors, limiting the attained restoration quality. To address this, two different priors are adopted in separated stages to restore an image in a coarse-to-fine manner and each stage follows a variational regularization scheme. In the first stage, salient edges and large scale textures are produced by minimizing the e 0 norm of gradient. The intermediate result is then refined by non-local auto regression model in the next stage. Finally, experimental results demonstrate that the proposed methodology is efficient and achieves nice performance.