A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Guanlei Xu, Xiaotong Wang and Xiaogang Xu, "Single Image Enhancement in Sandstorm Weather via Tensor Least Square," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1649-1661, Nov. 2020. doi: 10.1109/JAS.2020.1003423
Citation: Guanlei Xu, Xiaotong Wang and Xiaogang Xu, "Single Image Enhancement in Sandstorm Weather via Tensor Least Square," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1649-1661, Nov. 2020. doi: 10.1109/JAS.2020.1003423

Single Image Enhancement in Sandstorm Weather via Tensor Least Square

doi: 10.1109/JAS.2020.1003423
Funds:  This work was supported by the National Natural Science Foundation of China (61771020, 61471412, 2019KD0AC02)
More Information
  • In this paper, we present a tensor least square based model for sand/sandstorm removal in images. The main contributions of this paper are as follows. First, an important intrinsic natural feature of outdoor scenes free of sand/sandstorm is found that the outlines in RGB channels are somewise similar, which discloses the physical validation using the tensor instead of the matrix. Second, a tensor least square optimization model is presented for the decomposition of edge-preserving base layers and details. This model not only decomposes the color image (taken as an inseparable indivisibility) in X, Y directions, but also in Z direction, which meets the statistical feature of natural scenes and can physically disclose the intrinsic color information. The model’s advantages are twofold: one is the decomposition of edge-preserving base layers and details that can be employed for contrast enhancement without artificial halos, and the other one is the color driving ability that makes the enhanced images as close to natural images as possible via the inherent color structure. Thirdly, the tensor least square optimization model based image enhancement scheme is discussed for the sandstorm weather images. Finally, the experiments and comparisons with the state-of-the-art methods on real degraded images under sandstorm weather are shown to verify our method’s efficiency.

     

  • loading
  • [1]
    K. M. He, J. Sun, and X. O. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, Dec. 2011. doi: 10.1109/TPAMI.2010.168
    [2]
    R. Fattal, “Single image dehazing,” ACM Trans. Graph., vol. 27, no. 3, pp. 72, Aug. 2008.
    [3]
    D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process., vol. 6, no. 7, pp. 965–976, Jul. 1997. doi: 10.1109/83.597272
    [4]
    S. H. Chen and A. Beghdadi, “Natural enhancement of color image,” EURASIP J. Image Video Process., vol. 2010, pp. 175203, Aug. 2010.
    [5]
    X. L. Wu, “A linear programming approach for optimal contrast-tone mapping,” IEEE Trans. Image Process., vol. 20, no. 5, pp. 1262–1272, May 2011. doi: 10.1109/TIP.2010.2092438
    [6]
    C. O. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” IEEE Trans. Image Process., vol. 22, no. 8, pp. 3271–3282, Aug. 2013. doi: 10.1109/TIP.2013.2262284
    [7]
    R. T. Tan, “Visibility in bad weather from a single image,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 1−8.
    [8]
    S. G. Narasimhan and S. K. Nayar, “Chromatic framework for vision in bad weather,” in Proc. Conf. Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, 2000, pp. 598−605.
    [9]
    S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” Int. J. Comput. Vision, vol. 48, no. 3, pp. 233–254, Jul. 2002. doi: 10.1023/A:1016328200723
    [10]
    S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 6, pp. 713–724, Jul. 2003. doi: 10.1109/TPAMI.2003.1201821
    [11]
    S. G. Narasimhan and S. K. Nayar, “Interactive (de)weathering of an image using physical models,” in Proc. Workshop on Color and Photometric Methods in Computer Vision, France, 2003.
    [12]
    S. K. Nayar and S. G. Narasimhan, “Vision in bad weather,” in Proc. 7th IEEE Int. Conf. Computer Vision, Kerkyra, Greece, 1999, pp. 820−827.
    [13]
    K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in Graphics Gems IV, P. S. Heckbert, Ed. San Diego, CA, United States: Academic Press Professional, Inc., 1994, pp. 474−485.
    [14]
    K. M. He, J. Sun, and X. O. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397–1409, Jun. 2013. doi: 10.1109/TPAMI.2012.213
    [15]
    Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” in Proc. SIGGRAPH’08: ACM SIGGRAPH 2008, Los Angeles California, 2008, Article No.: 67.
    [16]
    R. L. Lagendijk, J. Biemond, and D. E. Boekee, “Regularized iterative image restoration with ringing reduction,” IEEE Trans. Acoust. Speech Signal Process., vol. 36, no. 12, pp. 1874–1888, Dec. 1988. doi: 10.1109/29.9032
    [17]
    K. N. Nordström, “Biased anisotropic diffusion: a unified regularization and diffusion approach to edge detection,” EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-89–514, May 1989.
    [18]
    K. Subr, C. Soler, and F. Durand, “Edge-preserving multiscale image decomposition based on local extrema,” ACM Trans. Graph., vol. 28, no. 5, pp. 147, Dec. 2009.
    [19]
    A. Lvin, D. Lischinski, and Y. Weiss, “Colorization using optimization,” ACM Trans. Graph., vol. 23, no. 3, pp. 689–694, Aug. 2004. doi: 10.1145/1015706.1015780
    [20]
    J. P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proc. 12th IEEE Conf. Computer Vision, Kyoto, Japan, 2009, pp. 2201−2208.
    [21]
    J. W. Ryde, “Echo intensities and attenuation due to clouds, rain, hail, sand and dust storms at centimetre wavelengths,” General Electric Company Research Laboratories, Wembley, 1941, pp. 22−24.
    [22]
    T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Rev., vol. 51, no. 3, pp. 455–50, Aug. 2009. doi: 10.1137/07070111X
    [23]
    X. Y. Fu, Y. Huang, D. L. Zeng, X. P. Zhang, and X. H. Ding, “A fusion-based enhancing approach for single sandstorm image,” in Proc. IEEE 16th Int. Workshop on Multimedia Signal Processing (MMSP), Jakarta, Indonesia, 2014, pp. 1−5.
    [24]
    G. L. Xu, X. T. Wang, L. M. Shao, L. J. Zhou, and X. G. Xu, “Low visibility weather recognition via SVM and decision tree in single image,” Image and Signal Processing, vol. 5, no. 4, pp. 155–165, Oct. 2016.
    [25]
    X. D. Zhang, Matrix Analysis and Applications. Beijing: Tsinghua University Press, 2004.
    [26]
    R. C. Gonzalez and R. E. Woods, Digital Image Processing. 2nd ed. New Jersey: Pearson Education, 2003.
    [27]
    X. D. Zhang, Modern Signal Processing. 2nd ed. Beijing: Tsinghua University Press, 2002.
    [28]
    D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, Oct. 1999. doi: 10.1038/44565
    [29]
    G. L. Xu, X. T. Wang, L. T. Wang, B. Liu, S. P. Su, and X. G. Xu, “Generalized uncertainty principles associated with Hilbert transform,” Signal Image Video Process., vol. 8, no. 2, pp. 279–285, Feb. 2014. doi: 10.1007/s11760-013-0547-x
    [30]
    G. L. Xu, X. T. Wang, and X. G. Xu, “Entropic uncertainty inequalities on sparse representation,” IET Signal Process., vol. 10, no. 4, pp. 413–421, Jun. 2016. doi: 10.1049/iet-spr.2014.0072
    [31]
    Q. Li, H. Tang, J. N. Chi, Y. Y. Xing, and H. T. Li, “Gesture segmentation with improved maximum between-cluster variance algorithm,” Acta Autom. Sinica, vol. 43, no. 4, pp. 528–537, Apr. 2017.
    [32]
    L. Li, Y. L. Lin, D. P. Cao, N. N. Zheng, and F.-Y. Wang, “Parallel learning —— A new framework for machine learning,” Acta Autom. Sinica, vol. 43, no. 1, pp. 1, Jan. 2017.
    [33]
    B. H. Chen, S. C. Huang, C. Y. Li, and S. Y. Kuo, “Haze removal using radial basis function networks for visibility restoration applications,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 8, pp. 3828–3838, Aug. 2018. doi: 10.1109/TNNLS.2017.2741975
    [34]
    B. L. Cai, X. M. Xu, K. Jia, C. M. Qing, and D. C. Tao, “DehazeNet: an end-to-end system for single image haze removal,” IEEE Trans. Image Process., vol. 25, no. 11, pp. 5187–5198, Nov. 2016. doi: 10.1109/TIP.2016.2598681
    [35]
    B. H. Chen and S. C. Huang, “Edge collapse-based dehazing algorithm for visibility restoration in real scenes,” J. Disp. Technol., vol. 12, no. 9, pp. 964–970, Sep. 2016. doi: 10.1109/JDT.2016.2552232
    [36]
    K. B. Gibson, D. T. Vo, and T. Q. Nguyen, “An investigation of dehazing effects on image and video coding,” IEEE Trans. Image Process., vol. 21, no. 2, pp. 662–673, Feb. 2012. doi: 10.1109/TIP.2011.2166968
    [37]
    B. H. Chen, S. C. Huang, and S. Y. Kuo, “Error-optimized sparse representation for single image rain removal,” IEEE Trans. Ind. Electron., vol. 64, no. 8, pp. 6573–6581, Aug. 2017. doi: 10.1109/TIE.2017.2682036
    [38]
    D. Eigen, D. Krishnan, and R. Fergus, “Restoring an image taken through a window covered with dirt or rain,” in Proc. IEEE Int. Conf. Computer Vision, Sydney, NSW, Australia, 2013, pp. 633−640.
    [39]
    B. H. Chen, S. C. Huang, and F. C. Chen, “A high-efficiency and high-speed gain intervention refinement filter for haze removal,” J. Disp. Technol., vol. 12, no. 7, pp. 753–759, Jul. 2016. doi: 10.1109/JDT.2016.2518646

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)

    Article Metrics

    Article views (905) PDF downloads(44) Cited by()

    Highlights

    • An important intrinsic natural feature of outdoor scenes free of sand/sandstorm is found.
    • A tensor least square optimization model is presented for the decomposition of edge-preserving base layers and details.
    • The tensor least square optimization model based image enhancement scheme is discussed for the sandstorm weather images.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return