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Image restoration with a high-order total variation minimization method
Authors:Xiao-Guang Lv  Yong-Zhong Song  Shun-Xu Wang  Jiang Le
Institution:1. School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu 210097, PR China;2. School of Science, Huaihai Institute of Technology, Lianyungang, Jiangsu 222005, PR China;3. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China
Abstract:In this paper, we propose a fast and efficient way to restore blurred and noisy images with a high-order total variation minimization technique. The proposed method is based on an alternating technique for image deblurring and denoising. It starts by finding an approximate image using a Tikhonov regularization method. This corresponds to a deblurring process with possible artifacts and noise remaining. In the denoising step, a high-order total variation algorithm is used to remove noise in the deblurred image. We see that the edges in the restored image can be preserved quite well and the staircase effect is reduced effectively in the proposed algorithm. We also discuss the convergence of the proposed regularization method. Some numerical results show that the proposed method gives restored images of higher quality than some existing total variation restoration methods such as the fast TV method and the modified TV method with the lagged diffusivity fixed-point iteration.
Keywords:Image restoration  Deblurring  Denoising  High-order  Regularization
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