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中南大学学报(自然科学版)

Journal of Central South University

第46卷    第6期    总第250期    2015年6月

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文章编号:1672-7207(2015)06-2065-09
基于bagging-rough SVM集成的去马赛克方法
贾晓芬,赵佰亭,周孟然,陈兆权,黄贤波

(安徽理工大学 电气与信息工程学院,安徽 淮南,232001)

摘 要: 能否有效保护图像的细节信息是衡量去马赛克技术优劣的关键因素,为改善图像细小边缘区域的边缘特征,抑制伪彩色效应或锯齿现象,提出一种基于bagging-rough的SVM集成算法,并利用该算法实现去马赛克。为提高支持向量机集成的预测精度,利用各平面之间的色彩相关性及色差恒定原理构建色差平面,在色差平面上利用图像的空间相关性构建原始样本集,采用bootstrap技术对原始样本集重取样,利用粗糙集约简算法约简重取样出的样本特征,然后用约简后的样本训练成员回归机、建立预测模型,将各成员回归机的预测结果采用均值法融合输出,其输出即为预测的色差平面上待插值点的色差,最后根据预测的色差值计算出丢失的像素值。仿真实验结果表明:所提去马赛克方法获得较优的客观指标彩色图像峰值信噪比(rCPSNR)和S-CIELAB的色差 ,较好的保护图像的细节信息,达到较满意的视觉效果。

 

关键字: 去马赛克;支持向量机;集成;粗糙集

Demosaicing based on bagging rough-set support vector machine ensemble
JIA Xiaofen, ZHAO Baiting, ZHOU Mengran, CHEN Zhaoquan, HUANG Xianbo

College of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

Abstract:A support vector machine (SVM) ensemble based demosaicing algorithm was proposed, which can reduce edge artifacts and false color artifacts effectively, and improve the image edge feature. Firstly, original sample set was constructed by applying inter-pixel correlation on green-red or green-blue color difference plane. The color difference plane was constructed by applying inter-channel correlation of the R, G, B channels and the constant color difference principle. Secondly, the training samples were selected by using the conventional bagging algorithm was used to re-sample the original sample set, and use the rough set reduction algorithm to dynamically reduce sample characteristics of the re-sampled samples. Thirdly, the individual support vector regression (SVR) was trained with the reduced training samples, and forecast the unknown color difference between two color channels (green-red or green-blue was forecast). Finally, the missing color value at each interpolated pixel was calculated using the forecasted color difference. Simulation results show that the proposed approach produces visually pleasing full-color result images and obtains higher rCPSNR and smaller colour difference of S-CIELAB than other conventional demosaicing algorithms.

 

Key words: demosaicing; support vector machine; ensemble; rough set

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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