混合梯度稀疏先验约束下的图像盲复原  

Blind image restoration method regularized by hybrid gradient sparse prior

作　　者：徐宁珊 王琛 任国强[1] 黄永梅[1] Xu Ningshan;Wang Chen;Ren Guoqiang;Huang Yongmei(Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu,Sichuan 610209,China;University of Chinese Academy of Sciences,Beijing 100049,China;Chengdu Office of Military Representative Bureau of Equipment Department of Aerospace System Department,Chengdu,Sichuan 610041,China)

机构地区：[1]中国科学院光电技术研究所,四川成都610209 [2]中国科学院大学,北京100049 [3]航天系统部装备部军代局成都室,四川成都610041

出　　处：《光电工程》2021年第6期58-69,共12页Opto-Electronic Engineering

基　　金：国家重点研发计划资助项目(2016YFB0500201)。

摘　　要：图像盲复原旨在无参考的情况下准确估计模糊核并恢复潜在的清晰图像。现有研究成果表明,利用全变分模型对高阶图像梯度先验约束进行描述可以有效抑制复原图像中产生的阶梯效应。本文在实验观察和研究的基础上,提出了采用稀疏先验约束模型对盲复原过程进行正则化的方法,以获得更佳的图像复原效果。该方法利用图像高阶梯度的稀疏性,通过与低阶梯度相结合来构造混合梯度正则项。同时,在正则项中引入基于图像熵的自适应因子,来调节迭代优化过程中两类梯度先验的比例,以此获得更好的收敛性。仿真与实验证明,与现有图像盲复原先进方法相比,本文方法具有更优越的图像复原性能。Blind image restoration aims to accurately estimate the blur kernel and the wanted clear image with no-reference.Existing researches show that the use of the Total Variation to model the high-order image gradient prior constraints can effectively suppress the blocking artifact generated in the restored image.On the basis of ex-perimental observation and research,this paper proposes to use the sparse prior constraint model to regularize the blind restoration process to obtain a better image restoration performance.Our method makes use of the sparsity of the high-order gradient of the image and combines it with the low-order gradient to construct the mixed gradient re-gularization term.At the same time,an adaptive factor based on image entropy is introduced to adjust the ratio of the two types of gradient priors in the iterative optimization process so as to obtain better convergence.Simulated and experimental results prove that compared with the existing state-of-the-art methods of blind image restoration,the proposed method has superior image restoration performance.

关 键 词：图像盲复原 高阶梯度 稀疏先验 自适应加权 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]