Efficient edge detection based on ghost imaging

Edge detection has been widely applied in computer vision and pattern recognition. Ghost imaging (GI) based edge detection can directly obtain the edges without first requiring the object image. In this paper, we propose an efficient edge detection method based on GI, where a novel variable size Sobel operator (called the V-Sobel operator) whose coefficients are isotropic and sensitive to all directions is first designed, and then “calculated speckles” are computed by the V-Sobel operator to keep the number of measurements in the GI system unchanged with the size of V-Sobel operator. Both experimental and simulation results have demonstrated the feasibility of the proposed edge detection method. Furthermore, compared with the edges obtained by GI based edge detection by using the Sobel operator, the edges acquired by the proposed method are clearer and more continuous even under a severely noisy environment. In particular, when the detection SNR is as low as 11.89dB, the proposed method can also achieve a complete and clear edge, while the method using Sobel operator cannot.

[1]  R. Boyd,et al.  "Two-Photon" coincidence imaging with a classical source. , 2002, Physical review letters.

[2]  Ling-An Wu,et al.  A double-threshold technique for fast time-correspondence imaging , 2013, 1311.3012.

[3]  Peng Mao,et al.  Surface-Plasmon-Polaritons-Assisted Enhanced Magnetic Response at Optical Frequencies in Metamaterials , 2016, IEEE Photonics Journal.

[4]  Dong Xiang,et al.  Edge detection based on computational ghost imaging with structured illuminations , 2018 .

[5]  Lei Zhang,et al.  Canny edge detection enhancement by scale multiplication , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jeffrey H. Shapiro,et al.  Computational ghost imaging , 2008, 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference.

[7]  Jingang Zhong,et al.  Single-pixel imaging by means of Fourier spectrum acquisition , 2015, Nature Communications.

[8]  Zhao Sheng-Mei,et al.  Correspondence normalized ghost imaging on compressive sensing , 2014 .

[9]  Jeffrey H. Shapiro,et al.  The physics of ghost imaging , 2012, Quantum Information Processing.

[10]  Hamid Krim,et al.  A Shearlet Approach to Edge Analysis and Detection , 2009, IEEE Transactions on Image Processing.

[11]  Xiquan Fu,et al.  Reduction of the defocusing effect in lensless ghost imaging and ghost diffraction with cosh-Gaussian modulated incoherent sources. , 2018, Applied optics.

[12]  Shengmei Zhao,et al.  Fast reconstructed and high-quality ghost imaging with fast Walsh–Hadamard transform , 2016 .

[13]  Shengmei Zhao,et al.  Edge detection based on subpixel-speckle-shifting ghost imaging , 2018 .

[14]  Qionghai Dai,et al.  Efficient single pixel imaging in Fourier space , 2015, 1504.03823.

[15]  Wenlin Gong,et al.  A method to improve the visibility of ghost images obtained by thermal light , 2010 .

[16]  Werner Frei,et al.  Fast Boundary Detection: A Generalization and a New Algorithm , 1977, IEEE Transactions on Computers.

[17]  A. Gatti,et al.  Differential ghost imaging. , 2010, Physical review letters.

[18]  E Tajahuerce,et al.  Signal-to-noise ratio of single-pixel cameras based on photodiodes. , 2018, Applied optics.

[19]  Shengmei Zhao,et al.  Edge detection based on single-pixel imaging. , 2018, Optics express.

[20]  J. Shapiro,et al.  Normalized ghost imaging , 2012, 1212.5041.

[21]  R. Boyd,et al.  An introduction to ghost imaging: quantum and classical , 2017, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  Shengmei Zhao,et al.  Optical image hiding based on computational ghost imaging , 2016 .

[23]  Xue-Feng Liu,et al.  Edge detection based on gradient ghost imaging. , 2015, Optics express.