A Fast Blind Deblurring Algorithm Using Local Gradient Product Prior

Jixuan Liang, Yanshan Li

Abstract: Blind image deblurring is the restoration of latent clear images from blurred images without knowing the blur kernel. Recently, a large number of priors have been proposed to effectively address the ill-posed nature of blind deblurring. However, most methods approximate the proposed priors and only use the first-order term of the blurred image. We observe the gradient inner product of clear images is significantly larger than that of blurry ones. In this paper, we propose a novel Local Gradient Product (LGP) prior based on this observation. This prior not only offers a more accurate approximation but also uses the relationship between image pixels involving quadratic terms. The employment of the LGP prior avoids the inversion of large matrices, thereby improving solution efficiency. Extensive experiments demonstrate that our algorithm achieves state-of-the-art performance and competitive running speed on benchmark datasets. Our MATLAB code and experimental results are available at github.com/JixuanLiang/deblur-LGP.