东南大学学报（自然科学版）2016，Vol.46Issue(6)：1143-1148,6.DOI:10.3969/j.issn.1001-0505.2016.06.006
基于马尔科夫随机场学习模型的图像模糊核估计
Image blur kernel estimation based on Markov random field learning model
摘要
Abstract
To make the most of image's regional feature and structural information as the prior knowledge in estimating blur kernel,an estimation method for blur kernel based on the Markov ran-dom field learning model is proposed.First,a node set in the Markov random field is constituted by sliding sub-window,and the image characteristics of each sub-window,such as the response of multi-curvature orientation energy filter and edge distribution,are extracted as the input vector. Then,model parameters are estimated by the logarithmic pseudo-likelihood optimization algorithm, and the training samples are labeled by adopting the cross entropy similarity to measure blur kernel's similarity.Finally,the optimal image sub-window is inferred based on the loopy belief propagation algorithm.Both synthetic and real blurred images are tested by the proposed method.The experi-mental results show that the method can accurately estimate blur kernel,and achieves favorable effects both in subjective visual contrast and objective evaluation.Meanwhile,the method also has a strong self-adaptability.Compared with the other three methods,the blur kernel similarity is im-proved by 1.55%,5.64% and 7.02%,respectively.关键词
图像恢复/模糊核/马尔科夫随机场/核相似性Key words
image restoration/blur kernel/Markov random field/kernel similarity分类
信息技术与安全科学引用本文复制引用
何富运,张志胜..基于马尔科夫随机场学习模型的图像模糊核估计[J].东南大学学报（自然科学版）,2016,46(6):1143-1148,6.基金项目
国家自然科学基金资助项目（51275090）、国家自然科学基金科学仪器基础研究专款资助项目（21327007）、中央高校基本科研业务费专项资金资助项目、江苏省普通高校研究生科研创新计划资助项目（KYLX15＿0208）． （）
