一种基于量子机制的图像显著性检测方案（英文）
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摘要
提出了一种与传统方法相比效率更高的量子图像显著性检测方案。为了在量子计算机中表示和存储RGB图像,并计算不同像素间的反差,此方案采用3量子位描述颜色信息,把2~(2n)×2图像矩阵编码为量子叠加态;结合Hadamard门和受控旋转算子,计算基态概率幅可反映像素在RGB三通道上的全局颜色反差;通过有限次数的投影测量可得到像素的归一化颜色反差及位置信息,并构建显著图。给出了相关量子电路的实现和复杂度分析。与多种传统显著性检测算法进行了对比实验,结果表明提出的方案具有良好的检测效果和更高的检测效率。
A quantum image saliency detection scheme is proposed,which is more efficient than the traditional methods.In order to represent and store RGB images in quantum computers and calculate the contrast between different pixels,the scheme adopts three-qubits to describe color information.The 2~(2n) × 2image matrix is encoded as a quantum superposition state.Combined with Hadamard gate and controlled rotation operator,the global color contrast of pixels in RGB three-channels can be reflected by computing probability amplitude of basis states.The normalized color contrast and location information is achieved using a finite number of projective measurements,and the saliency map is constructed.The realization of related quantum circuits and complexity analysis are presented.The contrast experiments are carried out with several traditional saliency detection algorithms.Results show that the proposed scheme has good detection effect and higher detection efficiency.
A quantum image saliency detection scheme is proposed,which is more efficient than the traditional methods.In order to represent and store RGB images in quantum computers and calculate the contrast between different pixels,the scheme adopts three-qubits to describe color information.The 2~(2n) × 2image matrix is encoded as a quantum superposition state.Combined with Hadamard gate and controlled rotation operator,the global color contrast of pixels in RGB three-channels can be reflected by computing probability amplitude of basis states.The normalized color contrast and location information is achieved using a finite number of projective measurements,and the saliency map is constructed.The realization of related quantum circuits and complexity analysis are presented.The contrast experiments are carried out with several traditional saliency detection algorithms.Results show that the proposed scheme has good detection effect and higher detection efficiency.
引文
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[29]Zhang Yi,Lu Kai,Gao Yinghui.QSobel:A novel quantum image edge extraction algorithm[J].Science China Information Sciences,2015,58(1):130-143.
[2]Latorre J I.Image compression and entanglement[J].Quantum Physics,2005,1:0510031.
[3]Le P Q,Iliyasu A M,et al.A flexible representation and invertible transformations for images on quantum computers[C].New Advances in Intelligent Signal Processing,2011,372:179-202.
[4]Le P Q,Iliyasu A M,et al.Fast geometric transformations on quantum images[.J].International Journal of Applied Mathematics,2010,40(3):113-123.
[5]Le P Q,Iliyasu A M,et al.Efficient color transformations on quantum image[J].Journal of Formatics,2011,15(6):698-706.
[6]Caraiman S,Manta V I.Image segmentation on a quantum computer[J].Quantum Information Processing,2015,14(5):1693-1715.
[7]Sun Yangguang.Quantum statistical edge detection using path integral Monte Carlo simulation[C].Communications in Computer and Information Science,2014,472:430-434.
[8]Yan Fei,Le P Q,et al.Assessing the similarity of quantum images based on probability measurements[C].2012IEEE World Congress on Computational Intelligence,2012:1-6.
[9]Yan Fei,Iliyasu A M,et al.Quantum image searching based on probability distributions[J].Journal of Quantum Information Science,2012,2(3):55-60.
[10]Wang Ning,Lin Song.A watermarking strategy for quantum image based on least significant bit[J].Chinese Journal of Quantum Electronics(量子电子学报),2015,32(3):263-269(in Chinese).
[11]Su Feng,Liu Xiang,Long Huabao et al.Sampling number of reconstruction arithmetic based on quantum correlated imaging[J],Chinese Journal of Quantum Electronics(量子电子学报),2015,32(3):144-149(in Chinese).
[12]Borji A,Itti L.State-of-the-art in visual attention modeling[C].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):185-207.
[13]Zhai Yun,Shah Mubarak.Visual attention detection in video sequences using spatiotemporal cues[C].Proceedings of the 14th Annual ACM International Conference on Multimedia,2006:815824.
[14]Tong Na,Lu Huchuan,Zhang Lihe.Saliency detection with multi-scale super pixels[J].IEEE Signal Processing Letters,2014,21(9):1035-1039.
[15]Yang Chuan,Zhang Lihe,Lu Huchuan,et al.Saliency detection via graph-based manifold ranking[C].Computer Vision and Pattern Recognition,2013:3166-3172.
[16]Fan Qiang,Qi Chun.Two-stage salient region detection by exploiting multiple priors[J].Journal of Visual Communication and Image Representation,2014,25(8):1823-1834.
[17]Zhu Wangjiang,Liang Shuang,Wei Yichen.Saliency optimization from robust background detection[C].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2014:2814-2821.
[18]Xie Yulin,Lu Huchuan,Yang Ming-Hsuan.Bayesian saliency via low and mid level cues[J].Image Processing,2013,22(5):1689-1698.
[19]Jiang Bowen,Zhang Lihe,Lu Huchuan.Saliency detection via absorbing Markov chain[C].IEEE International Conference on Computer Vision,2013:1665-1672.
[20]Margolin Ran,Tal Ayellet,Zelnik-Manor Lihi.What makes a patch distinct?[C].Computer Vision and Pattern Recognition(CVPR),2013:1139-1146.
[21]Shi Keyang,Wang Keze,Lu Jiangbo,et al.Pisa:Pixelwise image saliency by aggregating complementary appearance contrast measures with spatial priors[C].Computer Vision and Pattern Recognition(CVPR),2013:2115-2122.
[22]Jiang Huaizu,Wang Jingdong,Yuan Zejian,et al.Salient object detection:A discriminative regional feature integration approach[C].Computer Vision and Pattern Recognition(CVPR),2013:2083-2090.
[23]Syamala Y,Tilak A V N.Reversible arithmetic logic unit[C].3th IEEE Int.Conf.on Electronics Computer Technology(ICECT),2011,5:207-211.
[24]Barenco A,Bennett C H,et al.Elementary gates for quantum computation[J].Phys.Rev.A,1995,52(5):3457.
[25]Achanta R,Hemamiz S,et al.Frequency-tuned salient region detection[C].IEEE Conf.on Computer Vision and Pattern Recognition(CVPR),2009:1597-1604.
[26]Goferman S,Zelnik-Manor L,Tal A.Context-aware saliency detection[J].IEEE Trans,on Pattern Analysis and Machine Intelligence,2012,34(10):1915-1926.
[27]Cheng M M,Zhang G X,et al.Global contrast based salient region detection[C].IEEE Conf.on Computer Vision and Pattern Recognition(CVPR),2011:409-416.
[28]Achanta R,Susstrunk S.Saliency detection using maximum symmetric surround[C].17th IEEE Int.Conf.on Image Processing(ICIP),2010:2653-2656.
[29]Zhang Yi,Lu Kai,Gao Yinghui.QSobel:A novel quantum image edge extraction algorithm[J].Science China Information Sciences,2015,58(1):130-143.