应用字典学习算法改善Bayer格式图像彩色恢复效果  

Improvement of Bayer-pattern Demosaicking with Dictionary Learning Algorithm

作　　者：朱波[1,2] 汶德胜[1] 王飞[3] 李华[4] 宋宗玺[1] 

机构地区：[1]中国科学院西安光学精密机械研究所,西安710119 [2]中国科学院研究生院,北京100049 [3]西安交通大学,西安710049 [4]商洛学院,商洛726000

出　　处：《电子与信息学报》2013年第4期812-819,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金重点项目(90920301);商洛学院科研基金(11SKY008)资助课题

摘　　要：利用单片探测器获取彩色图像,插值算法的优劣对结果起着决定性的作用。为了改善恢复效果,该文设计了一种基于字典学习的非线性Bayer格式图像彩色插值算法。根据图像梯度的变化,首先,在上下左右方向利用局部方向插值方法(LDI)对Bayer格式图像进行合并计算,用高斯混合模型(GMM)分类法训练字典,运用主分量分析(PCA)方法提取训练结果中的主要分量为学习提供样本,通过学习,得到R,B通道缺失的G分量。然后,应用G分量,插值得到另外两种缺失分量R和B,从而得到彩色图像。选取McMaster图像集作为字典,分别用算法对标准图像和使用DALSA公司彩色CMOS探测器开发的相机实际拍摄的图像进行插值恢复,较其它几种算法,视觉上伪彩色最少,峰值信噪比最优。整体性能优于现有的很多其它插值算法。Demosaicking is important for the quality of digital images in resource-constrained single chip devices. This paper presents an improved dictionary learning-based color demosaicking algorithm. Firstly, an initial interpolation is applied to the R, B channel by Local Directional Interpolation （LDI） and fused by analysis the joint distribution of the gradient. Gaussian Mixture Model （GMM）-based clustering is used to classify dictionary image into different classes. The Principal Component Analysis （PCA） is performed on these classes to choose the principal components for the dictionary construction. And then, dictionary learning is applied to obtain the interpolated G and the lost /~ and/~ are interpolated by the help of the reconstructed G, accordingly. Since R, G and B of the given pixels are better represented, the whole image can be reconstructed accurately. Taking McMaster color image dataset as dictionary, standard image and image from DALSA CMOS camera are used for effect evaluation of the demosaicking algorithm. Experimental results prove that the proposed algorithm outperforms some state-of-the-art demosaicking methods both in PSNR measure and visual quality.

关 键 词：图像处理 BAYER格式 去马赛克 字典学习 高斯混合模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]