通信学报2019，Vol.40Issue(1)：43-50,8.DOI:10.11959/j.issn.1000-436x.2019015
基于结构相似性的非参数贝叶斯字典学习算法
Nonparametric Bayesian dictionary learning algorithm based on structural similarity
摘要
Abstract
Though nonparametric Bayesian methods possesses significant superiority with respect to traditional comprehensive dictionary learning methods, there is room for improvement of this method as it needs more consideration over the structural similarity and variability of images. To solve this problem, a nonparametric Bayesian dictionary learning algorithm based on structural similarity was proposed. The algorithm improved the structural representing ability of dictionaries by clustering images according to their non-local structural similarity and introducing block structure into sparse representing of images. Denoising and compressed sensing experiments showed that the proposed algorithm performs better than several current popular unsupervised dictionary learning algorithms.关键词
非参数贝叶斯/字典学习/结构相似性/图像去噪/压缩感知Key words
nonparametric Bayesian/dictionary learning/structural similarity/denoising/compressed sensing分类
信息技术与安全科学引用本文复制引用
董道广,芮国胜,田文飚,康健,刘歌..基于结构相似性的非参数贝叶斯字典学习算法[J].通信学报,2019,40(1):43-50,8.基金项目
国家自然科学基金资助项目(No.41606117,No.41476089,No.61671016) （No.41606117,No.41476089,No.61671016）
