A Fast Algorithm for Image Restoration Based on Hessian Nuclear Norm Regularization
LIU Peng-fei1, XIAO Liang1,2
1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China;
2. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sensing, Nanjing, Jiangsu 210094, China
Recently, the Hessian Nuclear norm regularization method has been a preferable higher order regularization scheme for image restoration, but with the Hessian Nuclear norm regularization term been highly non-linear and non-differentiable, image deblurring and denoising processes are highly coupled so that their minimization algorithms are with highly computational complexity.In this paper, we employ variable splitting to design a fast alternating iterative minimization algorithm with closed-form solutions for image restoration, in which we separate image restoration into image deblurring and denoising.Furthermore, we show the convergence of our proposed algorithm.Finally, experimental results demonstrate the effectiveness of the proposed method which consists in not only giving the improved performance in terms of peak signal to noise ratio (PSNR), but also exhibiting a much faster convergence rate than the previous majorization-minimization (MM) method for Hessian Nuclear norm regularization based image restoration.
刘鹏飞, 肖亮. 基于Hessian核范数正则化的快速图像复原算法[J]. 电子学报, 2015, 43(10): 2001-2008.
LIU Peng-fei, XIAO Liang. A Fast Algorithm for Image Restoration Based on Hessian Nuclear Norm Regularization. Chinese Journal of Electronics, 2015, 43(10): 2001-2008.
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