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Underwater image colour constancy based on DSNMF

Underwater image colour constancy based on DSNMF

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Different wavelengths of light may undergo changes in underwater environment resulting in altered images. For example, the presence of floating particles causes underwater images to appear bluish and blurred. In this study, the authors propose a method called the deep sparse non-negative matrix factorisation (DSNMF) to estimate the illumination of an underwater image. The image under observation is divided into patches and each channel of a single patch is reshaped as an [ R, G, B ] matrix. The DSNMF method deeply factorises each input matrix into multiple layers with a sparseness constraint. The last layer of the factorised matrix is used as the illumination of the patch. The sparseness constraint adjusts the appearance of the final image. After factorisation, the estimated illumination is applied to each patch of the original image to obtain the final image. Compared with state-of-the-art underwater image enhancement methods using no reference image quality assessment, not only does the proposed method outperforms current techniques in terms of its visual effect and IQA, but is also simpler to implement.

References

    1. 1)
      • G. Artur , F. Andrzej , W. Mariusz .
        1. Artur, G., Andrzej, F., Mariusz, W.: ‘Experience with the use of a rigidly-mounted sidescan sonar in a harbour basin bottom investigation’, Ocean Eng., 2015, 109, pp. 439443.
        . Ocean Eng. , 439 - 443
    2. 2)
      • K.A. Skinner , M. Johnson-Roberson .
        2. Skinner, K.A., Johnson-Roberson, M.: ‘Detection and segmentation of underwater archaeological sites surveyed with stereo-vision platforms’. OCEANS'15 MTS/IEEE, Washington, October 2015, pp. 17.
        . OCEANS'15 MTS/IEEE , 1 - 7
    3. 3)
      • Y. Zhang , L. Liu , D. Sun .
        3. Zhang, Y., Liu, L., Sun, D., et al: ‘Single-carrier underwater acoustic communication combined with channel shortening and dichotomous coordinate descent recursive least squares with variable forgetting factor’, IET Commun., 2015, 9, (15), pp. 18671876.
        . IET Commun. , 15 , 1867 - 1876
    4. 4)
      • T. Fei , D. Kraus , A.M. Zoubir .
        4. Fei, T., Kraus, D., Zoubir, A.M.: ‘Contributions to automatic target recognition systems for underwater mine classification’, IEEE Trans. Geosci. Remote Sens., 2015, 53, (1), pp. 505518.
        . IEEE Trans. Geosci. Remote Sens. , 1 , 505 - 518
    5. 5)
      • J. Gao , A.A. Proctor , Y. Shi .
        5. Gao, J., Proctor, A.A., Shi, Y., et al: ‘Hierarchical model predictive image-based visual servoing of underwater vehicles with adaptive neural network dynamic control’, IEEE, 2016, 46, (10), pp. 23232334.
        . IEEE , 10 , 2323 - 2334
    6. 6)
      • R. Schettini , S. Corchs .
        6. Schettini, R., Corchs, S.: ‘Underwater image processing: state of the art of restoration and image enhancement methods’, EURASIP J. Adv. Signal Process., 2010, 2010, (1), pp. 114.
        . EURASIP J. Adv. Signal Process. , 1 , 1 - 14
    7. 7)
      • J.S. Jaffe .
        7. Jaffe, J.S.: ‘Computer modeling and the design of optimal underwater imaging systems’, IEEE J. Ocean. Eng., 1990, 15, (2), pp. 101111.
        . IEEE J. Ocean. Eng. , 2 , 101 - 111
    8. 8)
      • B.L. McGlamery .
        8. McGlamery, B.L.: ‘A computer model for underwater camera systems’. Ocean Optics VI, March 1980, pp. 221231.
        . Ocean Optics VI , 221 - 231
    9. 9)
      • W. Hou , A.D. Weidemann , D.J. Gray .
        9. Hou, W., Weidemann, A.D., Gray, D.J., et al: ‘Imagery-derived modulation transfer function and its applications for underwater imaging’. Optical Engineering+Applications, September 2007, p. 669622.
        . Optical Engineering+Applications , 669622
    10. 10)
      • W. Hou , D.J. Gray , A.D. Weidemann .
        10. Hou, W., Gray, D.J., Weidemann, A.D., et al: ‘Comparison and validation of point spread models for imaging in natural waters’, Opt. Express, 2008, 16, (13), pp. 99589965.
        . Opt. Express , 13 , 9958 - 9965
    11. 11)
      • E. Trucco , A.T. Olmos-Antillon .
        11. Trucco, E., Olmos-Antillon, A.T.: ‘Self-tuning underwater image restoration’, IEEE J. Ocean. Eng., 2006, 31, (2), pp. 511519.
        . IEEE J. Ocean. Eng. , 2 , 511 - 519
    12. 12)
      • L.J. Wang , J. Han , Y. Zhang .
        12. Wang, L.J., Han, J., Zhang, Y., et al: ‘Image fusion via feature residual and statistical matching’, IET Comput. Vis., 2016, 10, (6), pp. 551558.
        . IET Comput. Vis. , 6 , 551 - 558
    13. 13)
      • D.M. He , G.G. Seet .
        13. He, D.M., Seet, G.G.: ‘Divergent-beam lidar imaging in turbid water’, Opt. Lasers Eng., 2004, 41, (1), pp. 217231.
        . Opt. Lasers Eng. , 1 , 217 - 231
    14. 14)
      • S.G. Narasimhan , S.K. Nayar .
        14. Narasimhan, S.G., Nayar, S.K.: ‘Contrast restoration of weather degraded images’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (6), pp. 713724.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 713 - 724
    15. 15)
      • Y.Y. Schechner , Y. Averbuch .
        15. Schechner, Y.Y., Averbuch, Y.: ‘Regularized image recovery in scattering media’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (9), pp. 16551660.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 1655 - 1660
    16. 16)
      • H.Y. Chong , S.J. Gortler , T. Zickler .
        16. Chong, H.Y., Gortler, S.J., Zickler, T.: ‘The von Kries hypothesis and a basis for colour constancy’. IEEE 11th Int. Conf. on Computer Vision, October 2007, pp. 18.
        . IEEE 11th Int. Conf. on Computer Vision , 1 - 8
    17. 17)
      • B. Funt , H. Jiang .
        17. Funt, B., Jiang, H.: ‘Nondiagonal colour correction’. Int. Conf. on Image Processing, September 2003, p. I481.
        . Int. Conf. on Image Processing , I - 481
    18. 18)
      • K. Barnard , F. Ciurea , B. Funt .
        18. Barnard, K., Ciurea, F., Funt, B.: ‘Sensor sharpening for computational colour constancy’, J. Opt. Soc. Am. A, 2001, 18, (11), pp. 27282743.
        . J. Opt. Soc. Am. A , 11 , 2728 - 2743
    19. 19)
      • D.D. Lee , H.S. Seung .
        19. Lee, D.D., Seung, H.S.: ‘Algorithms for non-negative matrix factorization’. Advances in Neural Information Processing Systems, 2001, pp. 556562.
        . Advances in Neural Information Processing Systems , 556 - 562
    20. 20)
      • 20. Nvidia, C. U. D. A.: ‘Programming guide’, 2008.
        .
    21. 21)
      • Z. Chen , T. Jiang , Y. Tian .
        21. Chen, Z., Jiang, T., Tian, Y.: ‘Quality assessment for comparing image enhancement algorithms’. IEEE Conf. Computer Vision and Pattern Recognition, June 2014, pp. 30033010.
        . IEEE Conf. Computer Vision and Pattern Recognition , 3003 - 3010
    22. 22)
      • K. Panetta , C. Gao , S. Agaian .
        22. Panetta, K., Gao, C., Agaian, S.: ‘No reference colour image contrast and quality measures’, IEEE Trans. Consum. Electron., 2013, 59, (3), pp. 643651.
        . IEEE Trans. Consum. Electron. , 3 , 643 - 651
    23. 23)
      • M. Yang , A. Sowmya .
        23. Yang, M., Sowmya, A.: ‘An underwater color image quality evaluation metric’, IEEE Trans. Image Process. , 2015, 24, (12), pp. 60626071.
        . IEEE Trans. Image Process. , 12 , 6062 - 6071
    24. 24)
      • L. Chao , M. Wang .
        24. Chao, L., Wang, M.: ‘Removal of water scattering’. Second Int. Conf. on Computer Engineering and Technology, April 2010, pp. 3539.
        . Second Int. Conf. on Computer Engineering and Technology , 35 - 39
    25. 25)
      • H. Wen , Y. Tian , T. Huang .
        25. Wen, H., Tian, Y., Huang, T., et al: ‘Single underwater image enhancement with a new optical model’. IEEE Int. Symp. on Circuits and Systems, May 2013, pp. 753756.
        . IEEE Int. Symp. on Circuits and Systems , 753 - 756
    26. 26)
      • C. Ancuti , C.O. Ancuti , T. Haber .
        26. Ancuti, C., Ancuti, C.O., Haber, T., et al: ‘Enhancing underwater images by fusion’. ACM SIGGRAPH 2011 Posters, August 2011, p. 32.
        . ACM SIGGRAPH 2011 Posters , 32
    27. 27)
      • K. Gu , G. Zhai , M. Liu .
        27. Gu, K., Zhai, G., Liu, M., et al: ‘Brightness preserving video contrast enhancement using S-shaped transfer function’. Visual Communications and Image Processing, November 2013, pp. 16.
        . Visual Communications and Image Processing , 1 - 6
    28. 28)
      • K. Gu , G. Zhai , S. Wang .
        28. Gu, K., Zhai, G., Wang, S., et al: ‘A general histogram modification framework for efficient contrast enhancement’. IEEE Int. Symp. on Circuits and Systems, May 2015, pp. 28162819.
        . IEEE Int. Symp. on Circuits and Systems , 2816 - 2819
    29. 29)
      • K. Gu , G. Zhai , X. Yang .
        29. Gu, K., Zhai, G., Yang, X., et al: ‘Automatic contrast enhancement technology with saliency preservation’, IEEE Trans. Circuits Syst. Video Technol., 2015, 25, (9), pp. 14801494.
        . IEEE Trans. Circuits Syst. Video Technol. , 9 , 1480 - 1494
    30. 30)
      • K. Gu , G. Zhai , W. Lin .
        30. Gu, K., Zhai, G., Lin, W., et al: ‘The analysis of image contrast: from quality assessment to automatic enhancement’, IEEE Trans. Cybern., 2016, 46, (1), pp. 284297.
        . IEEE Trans. Cybern. , 1 , 284 - 297
    31. 31)
      • K. Gu , G. Zhai , X. Yang .
        31. Gu, K., Zhai, G., Yang, X., et al: ‘Deep learning network for blind image quality assessment’. IEEE Int. Conf. on Image Processing 2014, October 2014, pp. 511515.
        . IEEE Int. Conf. on Image Processing 2014 , 511 - 515
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