Blind image deconvolution via salient edge selection and mean curvature regularization

Abstract

Blind image deconvolution is an ill-posed problem since there exists infinite pairs of blur kernels and latent images. To obtain reasonable results of this problem, most previous methods have emphasized the importance of selecting salient edges for blur kernel estimation. In this paper, a blind deconvolution method based on explicit and implicit salient edges selection is proposed. Explicit edge selection is achieved by using mutually guided image filtering, while mean curvature regularization is adopted to remove disadvantageous structures implicitly. Besides, the proposed model can be efficiently optimized by using half-quadratic penalty method and mean curvature filter. Extensive experiments show that the proposed method performs favorably against the state-of-the-art blind deconvolution methods on both synthetic and real-world blurred images.