.png){kind=icon}
返回
UPRE method for total variation parameter selection
.png){kind=icon}
OA
AI
DOI：10.1016/j.sigpro.2010.02.025.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Total variation (TV) regularization is a popular method for solving a wide variety of inverse problems in image processing. In order to optimize the reconstructed image, it is important to choose a good regularization parameter. The unbiased predictive risk estimator (UPRE) has been shown to give a good estimate of this parameter for Tikhonov regularization. In this paper we propose an extension of the UPRE method to the TV problem. Since direct computation of the extended UPRE is impractical in the case of inverse problems such as deblurring, due to the large scale of the associated linear problem, we also propose a method which provides a good approximation of this large scale problem, while significantly reducing computational requirements. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Parameter selection
Total variation regularization
Large scale problem
Inverse problem
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Structure-texture image decomposition - Modeling, algorithms, and parameter selection结构-纹理图像分解-建模、算法和参数选择
PREAI
分享收藏
查看更多内容