小波变换和稀疏表示相结合的遥感图像融合  

Remote sensing image fusion with wavelet transform and sparse representation

作　　者：刘婷[1] 程建[1] 

机构地区：[1]电子科技大学电子工程学院,成都611731

出　　处：《中国图象图形学报》2013年第8期1045-1053,共9页Journal of Image and Graphics

基　　金：教育部高等学校博士学科点专项科研基金项目(20100185120021);电子科技大学中央高校基本科研业务费项目(ZYGX2009X003);电子科技大学青年科技基金重点项目(JX0804)

摘　　要：针对多光谱图像与全色图像的融合,提出一种结合小波变换和稀疏表示的融合算法。该算法充分利用小波变换具有保持光谱信息这一优势,首先对多光谱图像进行IHS(intensity-hue-satuation)变换,然后对亮度分量和全色图像进行单层小波变换,得到对应的高低频系数。分析高低频系数的特征,对于不能认为是"稀疏"的低频系数采用稀疏表示进行融合;对于可以认为是"稀疏"的高频系数采用图像信息融合规则进行融合。最后进行小波逆变换和IHS逆变换得到融合结果。实验结果表明,该算法最大限度地保留了光谱信息,并提高了空间分辨率。A remote sensing image fusion algorithm is presented based on wavelet transform and sparse representation for the fusion of multi-spectral image and panchromatic image. The algorithm makes full use of the wavelet transform which has the advantage of maintaining spectral information. First, the intensity-hue-saturation （IHS） transform is applied to the multi-spectral image. Then, the obtained corresponding high-and low-frequency coefficients are transformed by the monolayer wavelet transform on the intensity component and the panchromatic image. According to the different characteristics of the low and high frequency coefficients, the low-frequency coefficients cannot be considered to be ＂sparse＂. The low-frequency images are obtained their sparse coefficients through sparse representation. The high-frequency coefficients can be considered to be ＂sparse＂. A fusion rule, which uses the image information, was taken to compute the high fusion coefficients. Finally, the fused results are obtained through wavelet inverse transform and IHS inverse transform. The experimental results prove that the proposed method improve the spatial resolution and better maintain the spectral characteristics.

关 键 词：图像处理 图像融合 稀疏表示 小波变换 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]