.png){kind=icon}
返回
Parameter estimation in TV image restoration using variational distribution approximation
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2007.916051.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. Within the hierarchical Bayesian formulation, the reconstructed image and the unknown hyperparameters for the image prior and the noise are simultaneously estimated. The proposed algorithms provide approximations to the posterior distributions of the latent variables using variational methods. We show that some of the current approaches to TV-based image restoration are special cases of our framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included.
Keyword:
Bayesian methods
image restoration
parameter estimation
total variation (TV)
variational methods
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:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
Family Businesses in Spain: Tracks Followed and Outcomes Reached by Those Among the Largest Thousand
PREAI
分享收藏
查看更多内容