摘要
提出了一种基于自适应稀疏表示的多聚焦图像融合算法,该算法根据图像的结构特征将子块分为相似模型、平滑模型和细节模型.三种模型采取不同的处理,相似模型直接放入融合图像,平滑模型和细节模型分别采用加权平均法和稀疏表示法进行融合,从而减少了稀疏编码的图像块数,以提高融合效率.实验结果表明,该方法在保证融合图像主观效果和客观性能指标均优的的情况下,有效缩短了运算时间.
A multi-focus image fusion algorithm is given out,which is based on adaptive sparse representation.According to structure characteristics of image,the algorithm can divide sub-blocks into three models,which are the similar model,the smouth model and the detailed model.The three models get different process mode.The similar model is put into the fusion image directly.The smouth model uses the weighted average method and the detacled model uses sparse representation to realize fusion,so the quantity of blocks for sparse coding is reduced and the efficiecy is improved.The experiment results show that the way can shorten the operation time effectively,at the same time if can guarantee that the subjective effect and objective performance indexes of fused image are both optimal.
出处
《微电子学与计算机》
CSCD
北大核心
2015年第6期22-26,31,共6页
Microelectronics & Computer
基金
国家自然科学基金(61362021)
广西自然科学基金(2013GXNSFDA019030
2013GXNSFAA019331
2012GXNSFBA053014
2012GXNSFAA053231
2014GXNSFDA118035)
广西科技开发项目(桂科攻1348020-6
桂科能1298025-7)
广西教育厅项目(201202ZD044
2013YB091)
桂林市科技攻关(20130105-6
20140103-5)
桂林电子科技大学研究生教育创新计划资助项目(GDYCSZ201462)
关键词
稀疏表示
多聚焦图像融合
自适应
梯度值
sparse representation
multi-focus image fusion
adaptive
gradient value
