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陈凤娥-Image Deblurring Via Combined Total Variation and Framelet

发布日期:2014-06-16 11:24:43 阅读次数:[2643]次 作者:

核心提示:陈凤娥-Image Deblurring Via Combined Total Variation and Framelet

标题:Image Deblurring Via Combined Total Variation and Framelet
作者:Chen, FE (Chen, Fenge); Jiao, YL (Jiao, Yuling); Lin, LY (Lin, Liyu); Qin, QQ (Qin, Qianqing)
来源出版物:CIRCUITS SYSTEMS AND SIGNAL PROCESSING卷:33期:6页:1899-1916DOI:10.1007/s00034-013-9725-x出版年:JUN 2014
Web of Science 核心合集中的 "被引频次":0
被引频次合计:0
摘要:This paper presents a new approach to image deblurring, on the basis of total variation (TV) and wavelet frame. The Rudin-Osher-Fatemi model, which is based on TV minimization, has been proven effective for image restoration. The explicit exploitation of sparse approximations of natural images has led to the success of wavelet frame approach in solving image restoration problems. However, TV introduces staircase effects. Thus, we propose a new objective functional that combines the tight wavelet frame and TV to reconstruct images from blurry and noisy observations while mitigating staircase effects. The minimization of the new objective functional presents a computational challenge. We propose a fast minimization algorithm by employing the augmented Lagrangian technique. The experiments on a set of image deblurring benchmark problems show that the proposed method outperforms the previous state-of-the-art methods for image restoration.
地址:[Chen, Fenge; Lin, Liyu; Qin, Qianqing] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China.
[Jiao, Yuling] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China.
通讯作者地址:Chen, FE (通讯作者),Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China.
电子邮件地址:fengch@whu.edu.cn; laoba13781573969@163.com; foxery@126.com; qqqin@lmars.whu.edu

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