期刊文献+

基于量子行为粒子群优化算法的图像插值方法 被引量:1

Image interpolation algorithm based on quantum-behaved particle swarm optimization
下载PDF
导出
摘要 传统图像插值方法简单,容易实现,但经过插值后的图像会增加一定的虚假内容,导致图像模糊。为提高插值图像的质量和图像的分辨率,提出一种基于量子行为粒子群优化(QPSO)算法的图像插值方法。该方法利用QPSO算法在以传统插值图像为基础形成的解空间中,寻找符合目标函数的最优高分辨率图像。实验证明,该方法实用、可行,且能得到质量较好的插值图像。 The conventional interpolation algorithms of image are easy to be realized, but they resuh in high frequency artifacts in the interpolated image. In order to improve the quality of the interpolated image and enhance the resolution of it, an image interpolation algorithm based on Quantum-behaved Particle Swarm Optimization (QPSO) algorithm was proposed in this paper. This method uses QPSO algorithm to seek the best high resolution image through the objective function in the traditional interpolation image solution space. The experiments demonstrate that the proposed algorithm not only is practical and applicable, but also improves the quality of the interpolated images.
出处 《计算机应用》 CSCD 北大核心 2007年第9期2147-2149,共3页 journal of Computer Applications
基金 国家自然科学基金资助项目(60474030)
关键词 图像插值 粒子群优化 量子行为 image interpolation Particle Swarm Optimization (PSO) , quantum behaved
  • 相关文献

参考文献9

  • 1JEONG H,JUNG H.Regularized iterative image interpolation and its application to spatially scalable coding[J].IEEE Transactions on Consumer Electronics,1998,44(3):1042-1047.
  • 2刘志军,蔡超,彭晓明,周成平,丁明跃.一种新颖的基于遗传算法的正则化图像插值方法[J].中国图象图形学报(A辑),2004,9(8):934-940. 被引量:3
  • 3FRANS V.An analysis of particle swarm optimizes[D].Pretoria:University of Pretoria,2001.
  • 4CLERC M.The Swarm and Queen:Towards a deterministic and adaptive particle swarm optimization[C]//The 1999 Congress on Evolutionary Computation (CEC99).Washington:[s.n.],1999:1951-1957.
  • 5KENNEDY J,EBERHART R.Particle swarm optimization[C]// Proceeding IEEE International Conference on Neural Networks:IV.[S.l.]:IEEE Press,1995:1942-1948.
  • 6SUN J,FENG B,XU W.A global search strategy of quantum-behaved particle swarm optimization[C]// Proceeding 2004 Cybernetics and Intelligent Systems.[S.l.]:IEEE Press,2004:111-115.
  • 7SHI Y,EBERHART R C.Empirical study of particle swarm optimization[C]// The 1999 Congress on Evolutionary Computation (CEC99).Washington:[s.n.],1999.
  • 8SHI Y,EBERHART R C.A modified particle swarm optimizer[C]// Proceedings of International Conference on Evolutionary Computation.[S.l.]:IEEE Press,1998:69-73.
  • 9SUN J,FENG B,XU W B.Particle swarm optimization with particles having quantum behavior[C]// 2004 Congress on Evolutionary Computation (CEC 2004).Portland:[s.n.],2004:325-331.

二级参考文献8

  • 1玄光男(日) 程润伟.遗传算法与工程设计[M].北京:科学出版社,2000..
  • 2Chen Y-W, Nakao Z, Xue F, et al. A parallel genetic algorithm for image restoration[A]. In : Proceedings of the 13th International Conference on Pattern recognition[C]. Vienna, Austria,1996,3(4):694-698.
  • 3Moon G-K, Katsaggelos A K. General choise of the regularization functional in regularized image restoration[J]. IEEE Transactions on Image Proessing,1995,4(5):594-602.
  • 4Vogel C R, Oman M E. Fast , robust total variation-based reconstruction of noisy, blurred mages[J]. IEEE Transaction on Communications,1998,7(6):813-824.
  • 5Tekalp A M. Digital Video Processing[M]. Engelwood, New Jersey:Prentice Hall, 1995.
  • 6Jeong H-S,Jung H-J,Joon K-P, Regularized iterative image interpolation and its application to spatially scalable coding[J].IEEE Transanctions on Consumer Electronics, 1998,44(3):1042-1047.
  • 7Katsaggelos A K. Iterative image restoration algorithms[J].Optical Engineering, 1989,28(7):735-748.
  • 8刘志军,丁明跃,周成平,刘买利.基于并行遗传算法的图像超分辨率复原[J].中国图象图形学报(A辑),2004,9(1):62-68. 被引量:10

共引文献2

同被引文献7

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部