基于改进量子算法的模糊图像重建研究
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摘要
目的为了提高模糊图像重建质量,提出量子环型对称算法。方法首先建立量子环型对称模型,量子可与自身环上的任意量子信息交流,还可以与不同环上的任意量子信息交流。在环型对称中,同一环上所有量子的地位都是相同的,都具有相同的可计算性,消除了运算上的非低等级障碍。环与环之间的交流过程需要借助不同环上的共享量子实现,不同环之间的信息交流程度由环踞确定,环距较小,则交流较快,环距较大,则交流较慢。对各个环属性独立划分,即不同环上的量子属性不同,这样可避免将寻优能力较强的量子放入在同环中;接着对量子镜像、纠缠操作,量子可使用最大纠缠态而获得最大旋转角度,以便进行自适应快速进化。最后建立图像重建模型且给出了算法实现流程。结果实验仿真显示算法对模糊图像重建清晰,没有虚影和瑕疵,整体效果得到改善。算法的像素误差平均值最小,表明其重建图像接近原始清晰的图像,重建质量最好,算法执行时间最小,表明完成重建图像处理时间少。结论改进量子算法提高了模糊图像重建质量,同时量对称结构相当于并行运算,提高了执行效率。为模糊图像重建提供了一种新的参考方法。
Objective To improve the quality of fuzzy image reconstruction,a quantum ring symmetric algorithm is proposed.Methods First,the quantum loop symmetry model is established.The quantum can communicate with any quantum information on the ring and with any quantum information on different rings.In the ring symmetry,all the quantum positions on the same ring are the same.All of them have the same computability,eliminating the non-low grade obstacle in the operation and the exchange process between the ring.The ring needs to be different.The sharing quantum implementation on the ring is determined by the ring between the different rings.And if the ring is smaller,the communication is faster;if the ring is larger,the communication is slower.The quantum properties of each ring are divided independently,that is,the quantum properties of the different rings are different,so that the better Quantum Quantum can be avoided in the same ring;then in the quantum mirror and entanglement operation,quantum can be used maximumly.The maximum rotation angle of entangled state is obtained for adaptive and rapid evolution.Finally,an image reconstruction model is established and the algorithm implementation process is given.Results The simulation results show that the algorithm has a clear reconstruction of the fuzzy image without imaginary shadow and defect,and the overall effect is improved.The average pixel error of the algorithm is minimal,which indicates that the reconstructed image is close to the original and clear image,the reconstruction quality is the best,the algorithm execution time is least,and the reconstruction image processing time is less.Conclusion The improved quantum algorithm improves the quality of the fuzzy image reconstruction.At the same time,the symmetric structure is equivalent to the parallel operation and improves the execution efficiency.Therefore,it provides a new reference method for the fuzzy image reconstruction.
Objective To improve the quality of fuzzy image reconstruction,a quantum ring symmetric algorithm is proposed.Methods First,the quantum loop symmetry model is established.The quantum can communicate with any quantum information on the ring and with any quantum information on different rings.In the ring symmetry,all the quantum positions on the same ring are the same.All of them have the same computability,eliminating the non-low grade obstacle in the operation and the exchange process between the ring.The ring needs to be different.The sharing quantum implementation on the ring is determined by the ring between the different rings.And if the ring is smaller,the communication is faster;if the ring is larger,the communication is slower.The quantum properties of each ring are divided independently,that is,the quantum properties of the different rings are different,so that the better Quantum Quantum can be avoided in the same ring;then in the quantum mirror and entanglement operation,quantum can be used maximumly.The maximum rotation angle of entangled state is obtained for adaptive and rapid evolution.Finally,an image reconstruction model is established and the algorithm implementation process is given.Results The simulation results show that the algorithm has a clear reconstruction of the fuzzy image without imaginary shadow and defect,and the overall effect is improved.The average pixel error of the algorithm is minimal,which indicates that the reconstructed image is close to the original and clear image,the reconstruction quality is the best,the algorithm execution time is least,and the reconstruction image processing time is less.Conclusion The improved quantum algorithm improves the quality of the fuzzy image reconstruction.At the same time,the symmetric structure is equivalent to the parallel operation and improves the execution efficiency.Therefore,it provides a new reference method for the fuzzy image reconstruction.
引文
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[15] KAVITHA K,ARIVAZHAGAN S.Fuzzy inspired image classification algorithm for hyperspectral data using three-dimensional log-Gabor features[J].Optik-International Journal for Light and Electron Optics,2014,125(10):6236-6241.
[2] 贤光,颜昌翔,张新洁.运动模糊图像点扩散函数的频谱估计法[J].液晶与显示,2014,29(05):751-755.
[3] CHEN G G,LIU L L,SONG P Z,et al.Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems[J].Energy Conversion and Management,2014,86(08):548-560.
[4] KIM J L,WALKER J.Nonbinary quantum error-correcting codes from algebraic curves[J].Discrete Mathematics,2008,308(06):3115-3124.
[5] CLARK D,TONCHEV V D.Nonbinary quantum codes derived from finite geometries[J].Finite Fields and Their Applications,2012,18(01):63-69.
[6] TAKAHASHI K,KUROKAWA M,HASHIMOTO M.Multi-layer quantum neural network controller trained by real-coded genetic algorithm[J].Neurocomputing,2014,134(06):159-164.
[7] 肖海林,欧阳缮,谢武.量子Turbo乘积码[J].物理学报,2011,60(02):15-21.
[8] GAMALLO P,GONZáLEZ M,HUARTE -LARRANAGA F.Performance analysis of two quantum reaction dynamics codes:time-dependent and time-independent strategies[J].Procedia Computer Science,2013,18(06):835-844.
[9] LI R H,LI X L.Binary construction of quantum codes of minimum distances five and six[J].Discrete Mathematics,2008,308(05):1603-1611.
[10] BANICA T,COLLINS B.Integration over the Pauli quantum group[J].Journal of Geometry and Physics,2008,58(8):942-961.
[11] LIN Q,BAI Y K,YE M Y,et al.Measurement-induced nonlocality in the W and Greenberger-Horne-Zeilinger superposition states[J].Chinese Physics B,2015,24(3):61-65.
[12] 叶明勇.量子门操作与量子纠缠[D].安徽:中国科学技术大学,2007.
[13] 丁卫平,王建东,管致锦,等.基于量子云模型演化的最小属性约简增强算法[J].东南大学学报(自然科学版),2013,43(2):290-295.
[14] BALASUBRAMANIAM P,ANANTHI V P.Image fusion using intuitionistic fuzzy sets[J].Information Fusion,2014,20(10):21-30.
[15] KAVITHA K,ARIVAZHAGAN S.Fuzzy inspired image classification algorithm for hyperspectral data using three-dimensional log-Gabor features[J].Optik-International Journal for Light and Electron Optics,2014,125(10):6236-6241.