Single-pixel compressive imaging based on the transformation of discrete orthogonal Krawtchouk moments.

A single-pixel compressive imaging technique that uses differential modulation based on the transformation of discrete orthogonal Krawtchouk moments is proposed. In this method, two sets of Krawtchouk basis patterns are used to differentially modulate the light source, then the Krawtchouk moments of the target object are acquired from the light intensities measured by a single-pixel detector. The target image is reconstructed by applying an inverse Krawtchouk moment transform represented in the matrix form. The proposed technique is verified by both computational simulations and laboratory experiments. The results show that this technique can retrieve an image from compressive measurements and the real-time reconstruction. The background noise can be removed by the differential measurement to realize the excellent image quality. Moreover, the proposed technique is especially suitable for the single-pixel imaging application that requires the extraction of the characteristics at the region-of-interest.

[1]  Hassan Qjidaa,et al.  Fractional-order orthogonal Chebyshev Moments and Moment Invariants for image representation and pattern recognition , 2019, Pattern Recognit..

[2]  Kee Yuan,et al.  Computational-weighted Fourier single-pixel imaging via binary illumination. , 2018, Optics express.

[3]  Sasan Golabi,et al.  Non-unit mapped radial moments platform for robust, geometric invariant image watermarking and reversible data hiding , 2018, Inf. Sci..

[4]  Hongzhi Jiang,et al.  Projector-defocusing rectification for Fourier single-pixel imaging. , 2018, Optics express.

[5]  Arjuna Madanayake,et al.  Pruned Discrete Tchebichef Transform Approximation for Image Compression , 2018, Circuits Syst. Signal Process..

[6]  Syed Abdul Rahman Al-Haddad,et al.  Fast Recursive Computation of Krawtchouk Polynomials , 2018, Journal of Mathematical Imaging and Vision.

[7]  Jin Jin Liu,et al.  Prediction of phosphorylation sites based on Krawtchouk image moments , 2017, Proteins.

[8]  Rachid Benouini,et al.  3D image analysis by separable discrete orthogonal moments based on Krawtchouk and Tchebichef polynomials , 2017, Pattern Recognit..

[9]  Kebin Fan,et al.  Graphene metamaterial spatial light modulator for infrared single pixel imaging. , 2017, Optics express.

[10]  Zibang Zhang,et al.  Fast Fourier single-pixel imaging via binary illumination , 2017, Scientific Reports.

[11]  Zibang Zhang,et al.  Hadamard single-pixel imaging versus Fourier single-pixel imaging. , 2017, Optics express.

[12]  E. Tajahuerce,et al.  Single-pixel digital holography with phase-encoded illumination. , 2017, Optics express.

[13]  M. Padgett,et al.  Real-time imaging of methane gas leaks using a single-pixel camera. , 2017, Optics express.

[14]  Wen-Kai Yu,et al.  Compressive microscopic imaging with “positive–negative” light modulation , 2016 .

[15]  Wen-Kai Yu,et al.  Measurement dimensions compressed spectral imaging with a single point detector , 2016 .

[16]  Ling-An Wu,et al.  Coloured computational imaging with single-pixel detectors based on a 2D discrete cosine transform , 2016, 1603.02793.

[17]  S M Mahdi Khamoushi,et al.  Sinusoidal ghost imaging. , 2015, Optics letters.

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

[19]  Kevin J. Mitchell,et al.  Single-pixel infrared and visible microscope , 2014 .

[20]  David R. Smith,et al.  Terahertz compressive imaging with metamaterial spatial light modulators , 2014, Nature Photonics.

[21]  Prabin Kumar Bora,et al.  A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments , 2013, Pattern Recognit..

[22]  J. Howell,et al.  Photon-counting compressive sensing laser radar for 3D imaging. , 2011, Applied optics.

[23]  Faramarz Farahi,et al.  Active illumination single-pixel camera based on compressive sensing. , 2011, Applied optics.

[24]  Bo Fu,et al.  A symmetry and bi-recursive algorithm of accurately computing Krawtchouk moments , 2010, Pattern Recognit. Lett..

[25]  Dimitris E. Koulouriotis,et al.  Accurate and speedy computation of image Legendre moments for computer vision applications , 2010, Image Vis. Comput..

[26]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[27]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[28]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[29]  Raveendran Paramesran,et al.  Image analysis by Krawtchouk moments , 2003, IEEE Trans. Image Process..