引用本文:南栋,王志田,郑少华,等.一种基于稀疏系数匹配学习的图像去雾算法[J].控制与决策,2020,35(11):2797-2802
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一种基于稀疏系数匹配学习的图像去雾算法
南栋1, 王志田1, 郑少华1, 何林远2
(1. 陆军装甲兵学院蚌埠校区,安徽蚌埠233050;2. 空军工程大学航空工程学院,西安710038)
摘要:
针对现有基于先验假设的图像去雾算法无法普适性求解问题,提出一种基于稀疏系数匹配学习的图像去雾算法.该算法从图像复原角度出发,将雾天退化模型的求解转换为基于数据库的稀疏系数匹配.之后,从图像增强角度着手,将图像高亮区域对比度恢复量化为反馈迭代问题,进而有效提升图像的视觉效果.实验结果表明,所提出的算法在获得较好去雾结果的同时能够有效提升图像细节和对比度,并具有较强的适用性.
关键词:  图像去雾  雾天退化模型  稀疏表示  学习框架
DOI:10.13195/j.kzyjc.2018.1764
分类号:TP391
基金项目:国家自然科学基金项目(61701524);陆军装甲兵学院蚌埠校区自主立项课题项目(2018XQ21).
An image dehazing method based on learning framework with sparse coefficient matching
NAN Dong1,WANG Zhi-tian1,ZHENG Shao-hua1,HE Lin-yuan2
(1. Bengbu Campus,Academy of Army Armored Forces,Bengbu233050, China;2. Institute of Aeronautics,Air Force Engineering University,Xián710038,China)
Abstract:
Due to the low accuracy of the existing image dehazing methods with prior, an image dehazing method based on a learning framework with sparse coefficient matching is proposed. Firstly, the solution of the hazy degradation model is transformed to sparse coefficient matching with the database from the view of image restoration. Then, to improve the visual effect of the result, a feedback iteration is quantified by the enhancement of the contrast in highlighted areas from the view of image enhancement. Experiments demonstrate that the proposed method can remove effectively haze as well as provide a good local detail, and it has good generality.
Key words:  image dehazing  hazy degradation model  sparse representation  learning framework

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