一种基于区域分割的多传感器图像融合方法

针对传统像素级图像融合方法割裂像素间联系的问题，提出一种基于区域分割的图像融合方法．在图像配准的基础上进行区域分割，提取显著性因子和可见性因子作为区域特征，自适应地确定融合权值以得到融合图像．该方法克服了传统像素级融合方法中融合图像模糊、对噪声敏感等不足，尤其适用于图像没有严格配准的应用场合．实验结果表明，与小波变换融合方法相比，均方根误差减小了33％～36％，灰度标准差提高了0．9％～53％，互信息提高了39％～42％，空间频率提高了0．3％～38％，客观评价准则与目视效果吻合良好．

Region Segmentation Based Multisensor Image Fusion Approach

Focusing on the most traditional image fusion algorithms that split relationship among pixels, a region based image fusion scheme was proposed. On the basis of image registration, source images were segmented into several regions, then two features of salience and visibility were extracted from each region to adaptively determine the fusion weight to get the fused image. The proposed method solves some of the well-known problems in pixel-level fusion, such as blurring effects and high sensitivity to noise, particularly when there is mis-registration of the source images. Experimental results show that it outperforms the discrete wavelet transform approach with 33% to 36% reduction in root mean square error, 0. 9% to 53% improvement in standard deviation of gray level, 39%/40 to 42% enhancement in mutual information, and 0. 3 % to 38 % improvement in space frequency. The objective evaluation criteria coincide with the visual effect very well.