Quantum Image Edge Extraction Based on Improved Sobel Operator

Edge extraction is a basic task in image processing. This paper proposes a quantum image edge extraction algorithm based on improved sobel operator for the generalized quantum image representation (GQIR) to solve the real-time problem. The quantum image model of GQIR can store arbitrary quantum images with a size of H × W. Our scheme can calculate the gradients of image intensity of all the pixels simultaneously. Then, the concrete circuits of quantum image edge extraction algorithm are implemented by using a series of quantum operators which have been designed. Compared with existing quantum edge extraction algorithms, our scheme can achieve more accurate edge extraction, especially for diagonal edges. Finally, the complexity of the quantum circuits were been analyzed based on the basic quantum gates and give the simulation experiment results on classical computer.

[1]  Nan Jiang,et al.  Quantum image scaling using nearest neighbor interpolation , 2015, Quantum Inf. Process..

[2]  Abdullah M. Iliyasu,et al.  Fast Geometric Transformations on Quantum Images , 2010 .

[3]  Kai Lu,et al.  NEQR: a novel enhanced quantum representation of digital images , 2013, Quantum Information Processing.

[4]  N. Ranganathan,et al.  Design of Efficient Reversible Binary Subtractors Based on a New Reversible Gate , 2009, 2009 IEEE Computer Society Annual Symposium on VLSI.

[5]  W. Ruch,et al.  Sensation Seeking, General Aesthetic Preferences, and Humor Appreciation as Predictors of Liking of the Grotesque , 2009 .

[6]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.

[7]  Qingxin Zhu,et al.  Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state , 2014, Quantum Inf. Process..

[8]  Salvador E. Venegas-Andraca,et al.  Processing images in entangled quantum systems , 2010, Quantum Inf. Process..

[9]  Muhammad Mahbubur Rahman,et al.  Low Cost Quantum Realization of Reversible Multiplier Circuit , 2009 .

[10]  Barenco,et al.  Elementary gates for quantum computation. , 1995, Physical review. A, Atomic, molecular, and optical physics.

[11]  Himanshu Thapliyal,et al.  A new design of the reversible subtractor circuit , 2011, 2011 11th IEEE International Conference on Nanotechnology.

[12]  Sougato Bose,et al.  Storing, processing, and retrieving an image using quantum mechanics , 2003, SPIE Defense + Commercial Sensing.

[13]  R. Feynman Simulating physics with computers , 1999 .

[14]  Li Yu-he,et al.  Summary of image edge detection , 2005 .

[15]  Peng Liu,et al.  Using full duplex relaying in device-to-device (D2D) based wireless multicast services: a two-user case , 2014, Science China Information Sciences.

[16]  Nan Jiang,et al.  Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio , 2015, Quantum Information Processing.

[17]  Kai Lu,et al.  QSobel: A novel quantum image edge extraction algorithm , 2014, Science China Information Sciences.

[18]  Ping Fan,et al.  Quantum image edge extraction based on Laplacian operator and zero-cross method , 2019, Quantum Inf. Process..

[19]  Mingyue Ding,et al.  A new quantum edge detection algorithm for medical images , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[20]  Lov K. Grover A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[21]  Ping Fan,et al.  Quantum image edge extraction based on classical Sobel operator for NEQR , 2018, Quantum Information Processing.

[22]  Kaoru Hirota,et al.  A flexible representation of quantum images for polynomial preparation, image compression, and processing operations , 2011, Quantum Inf. Process..

[23]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.