Image Sparse Decomposition Based on Quantum Genetic Algorithm
-
摘要: 为了减少图像稀疏分解的计算量,提出了一种基于量子遗传算法与匹配追踪相结合的图像稀疏分解快速算法.量子遗传算法能用较小的种群规模实现较大的空间搜索,全局寻优能力强,基于匹配追踪的图像稀疏分解是最优化问题,因此可用量子遗传算法快速实现.仿真结果表明,每步分解所需计算的图像或图像残差与原子的内积仅4 000次,由分解结果重建的图像具有较好的主观质量.Abstract: Based on the quantum genetic!algorithm(QGA)and the matching pursuit(MP),a fast image sparse decomposition algorithm was put forward to reduce the amount of calculation.QGA combining the genetic algorithm and the quantum information theory has a large search space with small population and a good global search capability,while image sparse decomposition based on MP is an optimal problem,so it can be fast solved by QGA.Simulation results show that the number of inner product between the image or its residual image and atoms is only 4 000 times in each calculaltion step,and the reconstructed image has fine visual quality.
-
Key words:
- image processing /
- sparse decomposition /
- matching pursuit /
- quantum genetic algorithm
-
MALLAT S,ZHANG Z.Matching pursuits with time-frequency dictionaries[J].IEEE Trans.Signal Processing,1993,41(12):3 397-3 415.[2] BERGEAU F,MALLAT S.Matching pursuit of images[C]//Proc.of IEEE-SP.Piladephia:IEEE Press,1994:330-333.[3] COIFMAN R,WICKERHAUSER M.Entropy-based algorithms for best basis selection[J].IEEE Trans.Information Theory,1992,38(2):1 713-1 716.[4] CHEN S,DONOHO D,SAUNERS M.Atom decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1999,20:33-61.[5] DAUBECHIES I.Time-frequency localization operator:A geometric phase space approach[J].IEEE Trans.Information Theory,1988,34(4):605-612.[6] OLSHAUSEN B,FIELD D.Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J].Nature,1996,381:607-609.[7] OLSHAUSEN B,FIELD D.Learning efficient linear codes for natural images:the roles of sparseness,overcompleteness,and statistical independence[C]//Proc.of SPIE,Human Vision and Electronic Imaging.San Jose:SPIE Press,1996,2 657:132-138.[8] 尹忠科,王建英,邵君.基于原子库结构特性的信号稀疏分解[J].西南交通大学学报,2005,40(2):173-178.YIN Zhongke,WANG Jianying,SHAO Jun.Sparse decomposition based on structural properties of atom dictionary[J].Journal of Southwest Jiaotong University,2005,40(2):173-178.[9] 华泽玺,尹忠科,黄雄华.信号在过完备库上分解中原子形成的快速算法[J].西南交通大学学报,2005,40(3):402-405.HUA Zexi,YIN Zhongke,HUANG Xionghua.Fast atom construction algorithm for signal decomposition in over-complete dictionary[J].Journal of Southwest Jiaotong University,2005,40(3):402-408.[10] NARAYANAN A,MOORE M.Quantum inspired genetic algorithm[C]//Proc.of the 1996 IEEE International Conference on Evolutionary Computation.Piscataway:IEEE Press,1996:61-66.[11] NARAYANAN A.An introductory tutorial to quantum computing[C]//Proc.of IEEE Colloquium on Quantum Computing:Theory,Applications and Implications.London:IEEPress,1997:1-3.[12] GUO Guangcan.Introduction to quantum information research[J].Physics China,2001,30(5):1 873-1 878.[13] NIELSEN M A,CHUANG I L.Quantum computation and quantum information[M].北京:高等教育出版社,2003:87-95.[14] NARAYANAN A,MOORE M.Quantum inspired genetic algorithms[C]//Proc.of the 1996 IEEE International Conference on Evolutionary Computation(ICEC96).Nogaya:IEEE Press 1996,41-46.[15] 张葛祥,金炜东.量子遗传算法的改进及其应用[J].西南交通大学学报,2003,38(6):717-722.ZHANG Gexiang,JIN Weidong.Improvement quantum genetic algorithm andits application[J].Journal of Southwest Jiaotong University,2003,38(6):717-722.[16] 杨俊安,解光军,庄镇泉.量子遗传算法及其在图像盲分离中的应用研究[J].计算机辅助设计与图形学学报,2003,15(7):847-852.YANG Jun'an,XIE Guangjun,ZHUANG Zhenquan.Quantum genetic algorithm and its application to blind image separation[J].Journal of Computer Aided Design Computer Graphics,2003,15(7):847-852.[17] VANDERGHEYNST P,FROSSARD P.Efficient image representation by anisotropic refinement in matching pursuit[C]// Proc.of IEEE on ICASSP.Salt Lake City:IEEE Perss,2001,3:1 757-1 760.
点击查看大图
计量
- 文章访问数: 1740
- HTML全文浏览量: 72
- PDF下载量: 490
- 被引次数: 0