Multi-scale Adaptive Computational Ghost Imaging

In some cases of imaging, wide spatial range and high spatial resolution are both required, which requests high performance of detection devices and huge resource consumption for data processing. We propose and demonstrate a multi-scale adaptive imaging method based on the idea of computational ghost imaging, which can obtain a rough outline of the whole scene with a wide range then accordingly find out the interested parts and achieve high-resolution details of those parts, by controlling the field of view and the transverse coherence width of the pseudo-thermal field illuminated on the scene with a spatial light modulator. Compared to typical ghost imaging, the resource consumption can be dramatically reduced using our scheme.

[1]  R. Boyd,et al.  High-order thermal ghost imaging , 2009, 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference.

[2]  J. Shapiro,et al.  Signal-to-noise ratio of Gaussian-state ghost imaging , 2008, 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference.

[3]  Mark R. Freeman,et al.  3D Computational Imaging with Single-Pixel Detectors , 2013 .

[4]  Wenlin Gong,et al.  Three-dimensional ghost imaging lidar via sparsity constraint , 2016, Scientific Reports.

[5]  A. Gatti,et al.  Ghost imaging with thermal light: comparing entanglement and classical correlation. , 2003, Physical review letters.

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

[7]  Graham M. Gibson,et al.  Simultaneous real-time visible and infrared video with single-pixel detectors , 2015, Scientific Reports.

[8]  Ling-An Wu,et al.  Adaptive compressive ghost imaging based on wavelet trees and sparse representation. , 2014, Optics express.

[9]  Wenlin Gong,et al.  Super-resolution far-field ghost imaging via compressive sampling , 2009, 0911.4750.

[10]  Enrong Li,et al.  Application of multi-correlation-scale measurement matrices in ghost imaging via sparsity constraints. , 2014, Applied optics.

[11]  Wenlin Gong,et al.  Three-dimensional ghost imaging lidar via sparsity constraint , 2013, Scientific Reports.

[12]  Chi Zhang,et al.  Object reconstitution using pseudo-inverse for ghost imaging. , 2014, Optics express.

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

[14]  O. Katz,et al.  Ghost imaging with a single detector , 2008, 0812.2633.

[15]  Shih,et al.  Optical imaging by means of two-photon quantum entanglement. , 1995, Physical review. A, Atomic, molecular, and optical physics.

[16]  A. Gatti,et al.  High-resolution ghost image and ghost diffraction experiments with thermal light. , 2005, Physical review letters.

[17]  A. Gatti,et al.  Three-dimensional coherence of light speckles: Experiment , 2009 .

[18]  L. Basano,et al.  Experiment in lensless ghost imaging with thermal light , 2006 .

[19]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[20]  A Gatti,et al.  Entangled imaging and wave-particle duality: from the microscopic to the macroscopic realm. , 2003, Physical review letters.

[21]  Federico Ferri,et al.  Longitudinal coherence in thermal ghost imaging , 2008 .

[22]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[23]  Robert W Boyd,et al.  Optimization of thermal ghost imaging: high-order correlations vs. background subtraction. , 2010, Optics express.

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

[25]  O. Katz,et al.  Compressive ghost imaging , 2009, 0905.0321.

[26]  R. Gerchberg.  A practical algorithm for the determination of phase from image and diffraction plane pictures , 1972 .

[27]  Ling-An Wu,et al.  Iterative denoising of ghost imaging. , 2014, Optics express.

[28]  Robert W Boyd,et al.  Quantum and classical coincidence imaging. , 2004, Physical review letters.

[29]  Amir Averbuch,et al.  Adaptive Compressed Image Sensing Using Dictionaries , 2012, SIAM J. Imaging Sci..

[30]  Huizu Lin,et al.  Sub-Rayleigh-diffraction imaging via modulating classical light. , 2015, Optics express.

[31]  Shensheng Han,et al.  Fourier-Transform Ghost Imaging with Hard X Rays. , 2016, Physical review letters.

[32]  Qionghai Dai,et al.  Multispectral imaging using a single bucket detector , 2015, Scientific Reports.

[33]  Manfred Bayer,et al.  Compressive adaptive computational ghost imaging , 2013, Scientific Reports.

[34]  Reza Kheradmand,et al.  Gray-scale and color optical encryption based on computational ghost imaging , 2012 .

[35]  Giuliano Scarcelli,et al.  Can two-photon correlation of chaotic light be considered as correlation of intensity fluctuations? , 2006, Physical review letters.

[36]  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.

[37]  Wenlin Gong,et al.  High-resolution far-field ghost imaging via sparsity constraint , 2015, Scientific Reports.

[38]  Giuliano Scarcelli,et al.  Phase-conjugate mirror via two-photon thermal light imaging , 2006 .

[39]  Ling-An Wu,et al.  Correlated two-photon imaging with true thermal light. , 2005, Optics letters.

[40]  Ting Zhang,et al.  Experimental quantum state tomography via compressed sampling. , 2012, Physical review letters.