期刊
SIGNAL PROCESSING
卷 105, 期 -, 页码 156-174出版社
ELSEVIER
DOI: 10.1016/j.sigpro.2014.04.031
关键词
Super-resolution; Adaptive norm; Mixed noise; Motion outliers; Adaptive weight estimation
资金
- Major State Basic Research Development Program [2011CB707105]
- National Natural Science Foundation of China [41271376]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT1278]
- Ph.D. Programs Foundation of Ministry of Education of China [20130141120001]
In this paper, we present a locally adaptive regularized super-resolution model for images with mixed noise and outliers. The proposed method adaptively assigns the local norms in the data fidelity term of the regularized model. Specifically, it determines different norm values for different pixel locations, according to the impulse noise and motion outlier detection results. The L-1 norm is employed for pixels with impulse noise and motion outliers, and the L-2 norm is used for the other pixels. In order to balance the difference in the constraint strength between the L-1 norm and the L-2 norm, a strategy to adaptively estimate a weighted parameter is put forward. The experimental results confirm the superiority of the proposed method for different images with mixed noise and outliers. (c) 2014 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据