Robust Image Deblurring Using Hyper Laplacian Model

Abstract

In recent years, many image deblurring algorithms have been proposed, most of which assume the noise in the deblurring process satisfies the Gaussian distribution. However, it is often unavoidable in practice both in non-blind and blind image deblurring, due to the error on the input kernel and the outliers in the blurry image. Without proper handing these outliers, the recovered image estimated by previous methods will suffer severe artifacts. In this paper, we mainly deal with two kinds of non-Gaussian noise in the image deblurring process, inaccurate kernel and compressed blurry image, and find that handling the noise as Laplacian distribution can get more robust result in these cases. Based on this point, the new non-blind and blind image deblurring algorithms are proposed to restore the clear image. To get more robust deblurred result, we also use 8 direction gradients of the image to estimate the blur kernel. The new minimization problem can be efficiently solved by the Iteratively Reweighted Least Squares(IRLS) and the experimental results on both synthesized and real-world images show the efficiency and robustness of our algorithm.