• Laser & Optoelectronics Progress
  • Vol. 57, Issue 8, 081018 (2020)
Jun Yang1、*, Bo Pan2, Li Chen1, Yongan Zhu1, Tao Jiang1, and Chen Cui3
Author Affiliations
  • 1College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing, Zhejiang 314001, China
  • 2Jiaxing Guodiantong New Energy Technology Co. LTD., Jiaxing, Zhejiang 314001, China
  • 3School of Data Science and Technology, Heilongjiang University, Harbin, Heilongjiang 150080, China
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    DOI: 10.3788/LOP57.081018 Cite this Article Set citation alerts
    Jun Yang, Bo Pan, Li Chen, Yongan Zhu, Tao Jiang, Chen Cui. Image Edge Information Aided Compressive Sampling Strategy[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081018 Copy Citation Text show less
    References

    [1] Romberg J. Compressive sensing by random convolution[J]. SIAM Journal on Imaging Sciences, 2, 1098-1128(2009).

    [2] Candes E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies?[J]. IEEE Transactions on Information Theory, 52, 5406-5425(2006).

    [3] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

    [4] Lustig M, Donoho D, Pauly J M. Sparse MRI: the application of compressed sensing for rapid MR imaging[J]. Magnetic Resonance in Medicine, 58, 1182-1195(2007).

    [5] Yuan J, Li Q, Gong W P. Influences of compressive sensing 3D reconstruction algorithm control parameters on terahertz digital holography reconstruction[J]. Chinese Journal of Lasers, 45, 1014001(2018).

    [6] Wang Y Y, Ren Y C, Chen L Y et al. Terahertz wave wide-beam imaging technology based on block compressive sensing theory[J]. Acta Optica Sinica, 39, 0407001(2019).

    [7] Luo L, Chen Q, Liu X J et al. Colored adaptive compressed imaging based on extended wavelet trees[J]. Laser & Optoelectronics Progress, 56, 010301(2019).

    [8] Wu X L, Dong W S, Zhang X J et al. Model-assisted adaptive recovery of compressed sensing with imaging applications[J]. IEEE Transactions on Image Processing, 21, 451-458(2012).

    [9] Wu X L, Zhang X J, Wang J. Model-guided adaptive recovery of compressive sensing. [C]∥2009 Data Compression Conference, March 16-18, 2009, Snowbird, Utah, USA. New York: IEEE, 123-132(2009).

    [10] Soni A, Haupt J. Learning sparse representations for adaptive compressive sensing. [C]∥2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 25-30, 2012, Kyoto, Japan. New York: IEEE, 2097-2100(2012).

    [11] Soni A, Haupt J. Efficient adaptive compressive sensing using sparse hierarchical learned dictionaries. [C]∥2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), November 6-9, 2011, Pacific Grove, CA, USA. New York: IEEE, 1250-1254(2011).

    [12] Mun S, Fowler J E. Block compressed sensing of images using directional transforms. [C]∥2009 16th IEEE International Conference on Image Processing (ICIP), November 7-10, 2009, Cairo, Egypt. New York: IEEE, 3021-3024(2009).

    [13] Do T T, Gan L, Nguyen N H et al. Fast and efficient compressive sensing using structurally random matrices[J]. IEEE Transactions on Signal Processing, 60, 139-154(2012).

    [14] Bioucas-Dias J M, Figueiredo M A T. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 16, 2992-3004(2007).

    [15] Becker S, Bobin J, Candès E J. NESTA: a fast and accurate first-order method for sparse recovery[J]. SIAM Journal on Imaging Sciences, 4, 1-39(2011).

    [16] Li C, Yin W, Zhang Y. User’s guide for TVAL3: TV minimization by augmented Lagrangian and alternating direction algorithms[M]. China: CAAM(2009).

    [17] Canny J. A computational approach to edge detection[M]. ∥Fischler M A, Firschein O. Readings in computer vision. California: Morgan Kaufmann Publishers, 184-203(1987).

    [18] Parker J R. Algorithms for image processing and computer vision[M]. UK: John Wiley & Sons, 42-46(2010).

    [19] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 60, 259-268(1992).

    [20] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 53, 4655-4666(2007).

    [21] Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 55, 2230-2249(2009).

    [22] Donoho D L, Tsaig Y, Drori I et al. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 58, 1094-1121(2012).

    [23] Hale E T, Yin W, Zhang Y. Fixed-point continuation for ℓ1-minimization: methodology and convergence[J]. SIAM Journal on Optimization, 19, 1107-1130(2008).

    [24] van den Berg E, Friedlander M P. Probing the pareto frontier for basis pursuit solutions[J]. SIAM Journal on Scientific Computing, 31, 890-912(2009).

    [25] Cand s E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 52, 489-509(2006).

    [26] Dai Q, the gradient-based recovery algorithms[J/OL]. -06-08)[2019-09-03]. https:∥arxiv.xilesou., top/abs/0906, 1487(2009).

    [27] Alliney S, Ruzinsky S A. An algorithm for the minimization of mixed l1 and ℓ2 norms with application to Bayesian estimation[J]. IEEE Transactions on Signal Processing, 42, 618-627(1994).

    [28] Osher S, Solé A, Vese L. Image decomposition and restoration using total variation minimization and the H 1[J]. Multiscale Modeling & Simulation, 1, 349-370(2003).

    [29] Hager W W, Zhang H. A survey of nonlinear conjugate gradient methods[J]. Pacific Journal of Optimization, 2, 35-58(2006).

    Jun Yang, Bo Pan, Li Chen, Yongan Zhu, Tao Jiang, Chen Cui. Image Edge Information Aided Compressive Sampling Strategy[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081018
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