粒子群优化的分块压缩感知影像融合
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摘要
针对传统像素级影像融合算法中存在的计算量大、融合系数不能自适应等问题,提出一种基于粒子群优化的分块压缩感知影像融合算法。该算法结合了压缩感知及粒子群优化算法的优点,通过对采样后的少数测量值进行融合减少了影像融合过程的计算量,同时通过全局寻优的特点可以确定最优融合系数。首先利用分块压缩感知(block-based compressive sensing,BCS)对待融合影像进行压缩采样,然后以粒子群优化算法确定的最优融合系数矩阵作为融合权值进行影像融合,最后通过正交匹配追踪算法进行影像重构并消除分块效应,得到融合后影像。该算法保证了融合效果的最优化选择,具备一定的自适应性。实验结果表明,基于粒子群优化的分块压缩感知影像融合取得了比传统影像融合算法更好的效果。
In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm,we propose a block-based compressive sensing image fusion method based on particle swarm optimization algorithm.The algorithm combines the compression of the image compression to reduce the amount of fusion data,as well as the global optimization of particle swarm optimization algorithm to determine the advantages of optimal parameters to improve the traditional algorithm of the existing problems.We get the compressive measurements of input images by blockbased compressive sensing(BCS)and fuse them with the rule of linear weighting,while the fusion coefficients of each block are selected by particle swarm optimization algorithm.In the iterative process,the image fusion coefficient is taken as particle,and the optimal value is obtained by combining the optimal objective function,taking the coefficient as the weight value.The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability.To evaluate the fused images,this paper uses five kinds of index parameters such as entropy,standard deviation,average gradient,degree of distortion and peak signal-to-noise ratio.The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm,we propose a block-based compressive sensing image fusion method based on particle swarm optimization algorithm.The algorithm combines the compression of the image compression to reduce the amount of fusion data,as well as the global optimization of particle swarm optimization algorithm to determine the advantages of optimal parameters to improve the traditional algorithm of the existing problems.We get the compressive measurements of input images by blockbased compressive sensing(BCS)and fuse them with the rule of linear weighting,while the fusion coefficients of each block are selected by particle swarm optimization algorithm.In the iterative process,the image fusion coefficient is taken as particle,and the optimal value is obtained by combining the optimal objective function,taking the coefficient as the weight value.The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability.To evaluate the fused images,this paper uses five kinds of index parameters such as entropy,standard deviation,average gradient,degree of distortion and peak signal-to-noise ratio.The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
引文
[1]王雪.基于压缩感知理论的遥感影像融合方法研究[D].北京:中国地质大学(北京),2015.
[2]杨娇.基于小波变换的图像融合算法的研究[D].北京:中国地质大学(北京),2014.
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[5]郭晶.基于压缩感知理论的卫星遥感图像融合算法研究[D].北京:北京交通大学,2013.
[6]GAN L.Block compressed sensing of natural images[C]∥Proceedings of the International Conference on Digital Signal Processing.Cardiff,UK,2007:403-406.
[7]杨森林,万国宾,高静怀.基于分块压缩感知的遥感图像融合[J].计算机应用研究,2015,32(1):316-320.
[8]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[9]张格森.压缩传感理论及若干应用技术研究[D].哈尔滨:哈尔滨工程大学,2012.
[10]ZHANG Y.Problems in the fusion of commercial high-resolution satellites images as well as LANDSAT 7images and initial solutions[J].Proceedings of the Isprs Cig&Sdh Joint International Symposium on Geospatial Theory Processing&Applications,2012,34(4):9-12.
[11]徐玉杰.粒子群算法的改进及应用[D].南京:南京师范大学,2013.
[12]李建勇.粒子群优化算法研究[D].浙江:浙江大学,2004.
[2]杨娇.基于小波变换的图像融合算法的研究[D].北京:中国地质大学(北京),2014.
[3]WAN T,CANAGARAJAH N,ACHIM A,et al.Compressive image fusion[C]∥international conference on image processing,2008:1308-1311.
[4]DIVEKAR A,ERSOY O.Image fusion by compressive sensing[C]∥Proceedings of the 17th International Conference on Geoinformatics,Fairfax,Va,USA,August 2009:1-6.
[5]郭晶.基于压缩感知理论的卫星遥感图像融合算法研究[D].北京:北京交通大学,2013.
[6]GAN L.Block compressed sensing of natural images[C]∥Proceedings of the International Conference on Digital Signal Processing.Cardiff,UK,2007:403-406.
[7]杨森林,万国宾,高静怀.基于分块压缩感知的遥感图像融合[J].计算机应用研究,2015,32(1):316-320.
[8]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[9]张格森.压缩传感理论及若干应用技术研究[D].哈尔滨:哈尔滨工程大学,2012.
[10]ZHANG Y.Problems in the fusion of commercial high-resolution satellites images as well as LANDSAT 7images and initial solutions[J].Proceedings of the Isprs Cig&Sdh Joint International Symposium on Geospatial Theory Processing&Applications,2012,34(4):9-12.
[11]徐玉杰.粒子群算法的改进及应用[D].南京:南京师范大学,2013.
[12]李建勇.粒子群优化算法研究[D].浙江:浙江大学,2004.