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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (6): 1164-1169    DOI: 10.3785/j.issn.1008-973X.2020.06.013
计算机技术     
基于马尔科夫专家场的泊松噪声图像去噪方法
贾真(),董文德*(),徐贵力,朱士鹏
南京航空航天大学 自动化学院，江苏 南京 211106
Image Poisson denoising algorithm based on Markov fields of experts
Zhen JIA(),Wen-de DONG*(),Gui-li XU,Shi-peng ZHU
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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摘要：

提出一种基于贝叶斯概率模型的泊松噪声图像去噪方法. 该方法基于贝叶斯最大后验概率模型框架，结合泊松概率分布，构建图像去噪模型. 考虑到马尔科夫随机场不能对复杂自然图像有效表征，引入高阶的马尔科夫专家场作为模型先验正则项，以表征图像自身概率分布. 利用二次惩罚函数，优化求解去噪模型，还原清晰图像. 将所提方法与其他去噪算法进行仿真实验对比，并采用峰值信噪比和结构相似性2种评价指标对去噪效果进行客观评价. 实验结果表明：与传统去噪方法相比，该方法的峰值信噪比至少提升了0.18 dB，去噪性能显著优于其他方法，能更好地保留图像的细节信息.
关键词： 泊松噪声;  马尔科夫专家场;  正则化;  图像去噪;  二次惩罚函数    
Abstract:

A Poisson noise image denoising method based on Bayesian probability model was proposed. An image denoising model was constructed based on Bayesian maximum a posteriori probability model and with combination of Poisson probability distribution. Considering that Markov random fields cannot represent complex natural images effectively, a higher-order Markov fields of experts was introduced as a prior regular term of the model to represent the probability distribution of the image. The quadratic penalty function was used to optimize the denoising model and restore clear images. The proposed method was compared with other denoising algorithms; the denoising effect was evaluated objectively by using two evaluation indexes: peak signal-to-noise ratio and structural similarity. The experimental results show that, compared with the traditional denoising methods, the peak signal-to-noise ratio of this method increased by at least 0.18 dB, and the denoising performance is significantly better than that of other methods. Thus, the details of the image can be retained better by using this mothed.
Key words: Poisson noise    Markov fields of experts    regularization    image denoising    quadratic penalty function
收稿日期: 2019-06-15 出版日期: 2020-07-06
CLC:  TN 911.73  
基金资助: 国家自然科学基金资助项目（61905112）；南京航空航天大学新教师工作启动基金资助项目（1003-YAH18066，1003-YAH18108）；南京航空航天大学青年科技创新基金资助项目（1003-XAC19008）；南京航空航天大学创新基地（实验室）开放基金资助项目（kfjj20180316）
通讯作者: 董文德     E-mail: jiazhen_95@163.com;dongwende@nuaa.edu.cn
作者简介: 贾真（1995—），女，硕士生，从事机器视觉、图像处理研究. orcid.org/0000-0002-8778-0673. E-mail： jiazhen_95@163.com
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引用本文:

贾真,董文德,徐贵力,朱士鹏. 基于马尔科夫专家场的泊松噪声图像去噪方法[J]. 浙江大学学报(工学版), 2020, 54(6): 1164-1169.

Zhen JIA,Wen-de DONG,Gui-li XU,Shi-peng ZHU. Image Poisson denoising algorithm based on Markov fields of experts. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1164-1169.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.06.013    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I6/1164

图 1  构成专家场（FoE）模型的典型邻域系统
图 2  二次惩罚函数误差−收敛曲线图
图 3  星云图像在9种去噪方法下的去噪结果局部图
图 4  8幅图像在不同去噪方法下的峰值信噪比对比
图 5  8幅图像在不同去噪方法下的结构相似度对比
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