文献类型：期刊文献

中文题名：基于狄利克雷过程混合模型的内外先验融合

英文题名：Integration of Internal and External Priors Based on Dirichlet Process Mixture Model

作者：张墨华[1,2];彭建华[1]

第一作者：张墨华

机构：[1]国家数字交换系统工程技术研究中心,郑州450000;[2]河南财经政法大学计算机与信息工程学院,郑州450000

第一机构：国家数字交换系统工程技术研究中心,郑州450000

年份：2020

卷号：47

期号：5

起止页码：172-180

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金(61841702);河南科技攻关计划项目(202102210371,1521002210087);河南省教育厅基金(14A520040)。

语种：中文

中文关键词：狄利克雷混合模型;图像复原;变分推理;批次更新;先验学习

外文关键词：Dirichlet process mixture model;Image restoration;Variational inference;Batch update;Prior learning

摘要：近年来,使用高斯混合模型作为块先验的贝叶斯方法取得了优秀的图像复原性能,针对这类模型分量固定及主要依赖外部学习的缺点,提出了一种新的基于狄利克雷过程混合模型的图像先验模型。该模型从干净图像数据库中学习外部通用先验,从退化图像中学习内部先验,借助模型中统计量的可累加性自然实现内外部先验融合。通过聚类的新增及归并机制,模型的复杂度随着数据的增大或缩小而自适应地变化,可以学习到可解释及紧凑的模型。为了求解所有隐变量的变分后验分布,提出了一种结合新增及归并机制的批次更新可扩展变分算法,解决了传统坐标上升算法在大数据集下效率较低、容易陷入局部最优解的问题。在图像去噪及填充实验中,相比传统方法,所提模型无论在客观质量评价还是视觉观感上都更有优势,验证了该模型的有效性。
In recent years,Bayesian approaches using Gaussian mixture model as a patch prior has achieved great success in image restoration.Aiming at the shortcomings of fix components and mainly relying on external learning of these models,a new image prior model based on Dirichlet process mixture model is proposed.The model learns external generic priors from a set of external clean images and learns internal priors from a given degraded image.Due to the accumulative property of the statistics in the mo-del,the integration of internal and external priori is naturally achieved in image restoration.Through the add and merge of cluster components,the model complexity can be adaptively changed as the data increases or decreases,more interpretable and more compact models can be learned.In order to solve the variational posterior of all hidden variables,a scalable variational algorithm combining with batch update with birth and merge mechanisms is proposed.The new algorithm improves the traditional coordinate ascent algorithm which is relatively inefficient under large data sets and often falls into the local optima.The effectiveness of the proposed model is verified by image denoising and inpainting experiments where the proposed model has advantage both on objective quality assessments and on visual perception comparing to traditional methods.