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Motion blur is an ill-posed problem which has been addressed for a long time in digital photographing. It usually happened under the circumstance that surrounding light is not enough. Several methods had been proposed for this problem. However, ringing is inevitable artifacts arising in the deconvolu-tion stage. To suppress undesirable artifacts, regularization based methods have been proposed using natural image priors to overcome the ill-posedness of de-convolution problem. These studies suggest that priors based on natural image statistics can regularize deblurring problems to yield better results.
In this paper, we propose a new single image deblurring algorithm based on hy-per-Laplacian priors. The hyper-Laplacian priors are applied to the PSF estima-tion process, which can precisely estimate blur kernel. Our method successfully reduces ringing artifacts while preserve edges. Experimental results show that our method has better image quality than other methods.
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