摘要
为了提高基于多尺度变换的多聚焦图像融合中聚焦区域的准确性,提出了一种基于非下采样Shearlet变换(NSST)与聚焦区域检测的多聚焦图像融合算法。首先,通过基于非下采样Shearlet变换的融合方法得到初始融合图像;其次,将初始融合图像与源多聚焦图像作比较,得到初始聚焦区域;接着,利用形态学开闭运算对初始聚焦区域进行修正;最后,在修正的聚焦区域上通过改进的脉冲耦合神经网络(IPCNN)获得融合图像。与经典的基于小波变换、Shearlet变换的融合方法以及当前流行的基于NSST和脉冲耦合神经网络(PCNN)的融合方法相比,所提算法在客观评价指标互信息(MI)、空间频率和转移的边缘信息上均有明显的提高。实验结果表明,所提出的算法能更准确地识别出源图像中的聚焦区域,能从源图像中提取出更多的清晰信息到融合图像。
To improve the accuracy of focusd regions in multifocus image fusion based on muhiscale transform, a multifocus image fusion algorithm was proposed based on NonSubsampled Shearlet Transform （NSST） and focused regions detection. Firstly, the initial fused image was acquired by the fusion algorithm based on NSST. Secondly, the initial focusd regions were obtained through comparing the initial fused image and the source multifocus images. And then, the morphological opening and closing was used to correct the initial focusd regions. Finally, the fused image was acquired by the Improved Pulse Coupled Neural Network （IPCNN） in the corrected focusd regions. The experimental results show that, compared with the classic image fusion algorithms based on wavelet or Shearlet, and the current popular algorithms based on NSST and Pulse Coupled Neural Network （PCNN）, objective evaluation criterions including Mutual Information （MI）, spatial frequency and transferred edge information of the propcsed method are improved obviously. The result illustrates that the proposed method can identify the focusd regions of source images more accurately and extract more sharpness information of source images to fusion image.
出处
《计算机应用》
CSCD
北大核心
2015年第2期490-494,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61362021)
广西省自然科学基金资助项目(2013GXNSFDA019030
2013GXNSFAA019331
2012GXNSFBA053014
2012GXNSFAA053231
2014GXNSFDA118035
桂科攻1348020-6
桂科能1298025-7)
广西教育厅重点项目(201202ZD044
2013YB091)
桂林市科技开发项目(20130105-6
20140103-5)
关键词
图像融合
多聚焦图像
非下采样剪切波变换
聚焦区域检测
形态学开闭运算
image fusion
multifocus image
NonSubsampled Shearlet Transform （NSST）
focused region detection
morphological opening and closing
