改进（2D）2PCA的彩色图像融合框架  

Color Image Fusion Framework Based on Improved （2D）2pCA

作　　者：夏余[1] 曲仕茹[1] 

机构地区：[1]西北工业大学自动化学院,陕西西安710129

出　　处：《光学学报》2014年第10期69-76,共8页Acta Optica Sinica

基　　金：航天科技创新基金(CASC201104);航空科学基金(2012ZC53043)

摘　　要：针对彩色图像融合时空间变换产生的色彩畸变以及红绿蓝(RGB)色彩空间各通道间的强相关性,同时考虑到基于主元分析(PCA)的图像融合算法存在图像结构利用率低、光谱信息损失多的缺点,提出了一种基于改进双向二维主元分析[(2D)2PCA]的图像融合框架。针对RGB色彩图像的结构特点,以待融合图像行、列方向的RGB分量作为基元进行二维主元分析(2DPCA),采用基于协方差的线性权重分配方法对融合图像进行重构,依照重构图像的结构特性进行主元替换,经基于协方差的加权逆变换得到融合图像。为验证算法的有效性进行了二次实验:1)是选取模糊彩色图像与对应的清晰灰度图像;2)是彩色可见光图像与对应的红外图像进行实验。实验结果表明使用该方法得到的融合图像可取得较好空间分辨率和理想的融合指标。Aiming at the color distortions generated by color space conversion and the strong correlation in the red green blue （RGB） space during image fusion process. The fusion framework is proposed based on the improved two directional two dimensional principal component analysis [（2D）2 PCA], which overtakes the shortcomings of PCA in catching image structure and reducing spectral information lost. Considering the structure of images in RGB space, the rows and columns of input images are set as the inputs of two 2DPCA approaches. The reconstruction weights of row and column directions are set linearly to the covariance. The PC replacemet is based on the structure properties of the reconstruction. The fusion is built by weighting reverse transformation of covariance. To verify the effectiveness of the proposed method, two experiments are discussed. One experiment uses the high resolution grey image and its responding blurred color image as source images, the other experiment is built on the visual color image and the infrared image. Experimental results show the superior of the proposed method over previous works with respect to the spatial resolution as well as other fusion indicators.

关 键 词：图像处理 双向二维主元分析 彩色图像融合 色彩畸变 红外图像 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术]