TAILIEUCHUNG - Báo cáo sinh học: " Research Article Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 398410 16 pages doi 2010 398410 Research Article Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage Liang Xiao 1 2 Li-Li Huang 1 3 and Badrinath Roysam2 1 School of Computer Science and Technology Nanjing University of Science and Technology Nanjing 210094 China 2 Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180-3590 USA 3 Department of Information and Computing Science Guangxi University of Technology Liuzhou 545000 China Correspondence should be addressed to Liang Xiao xiaoliang@ Received 27 December 2009 Revised 20 March 2010 Accepted 7 June 2010 Academic Editor Ling Shao Copyright 2010 Liang Xiao et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation TV functional. In order to suppress the staircasing effect and curvelet-like artifacts we use the multiscale curvelet shrinkage to compute an initial estimated image and then we propose a new gradient fidelity term which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts while preserving fine details. 1. Introduction Image

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