.png){kind=icon}
返回
Variational Bayesian Method for Retinex
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2014.2324813.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters. By assuming that the reflection function is piecewise continuous and illumination function is spatially smooth, we define the energy functions in the Gibbs distributions as a total variation function and a smooth function for the reflectance and the illumination, respectively. We then apply the variational Bayes approximation to obtain the approximation of the posterior distribution of unknowns so that the unknown images and hyperparameters are estimated simultaneously. Experimental results demonstrate the efficiency of the proposed method for providing competitive performance without additional information about the unknown parameters, and when prior information is added the proposed method outperforms the non-Bayesian-based Retinex methods we compared.
Keyword:
Bayesian methods
Retinex
image enhancement
parameter estimation
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
暂无机构信息
引用论文
Adaptive total variation image deblurring: A majorization-minimization approach.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏UAV-Assisted Privacy-Preserving Online Computation Offloading for Internet of Things无人机辅助的物联网隐私保护在线计算卸载
OAAI
OAAI分享收藏分享收藏
查看更多内容