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Review of underwater image restoration algorithms

Review of underwater image restoration algorithms

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Underwater images are susceptible to various distortions compared to images taken on land, due to the nature of the water environment. These images often suffer from diffraction, polarisation, absorption, scattering, colour loss and attenuation of light. Each part of the ocean will have its own sources of distortions, due to flickers caused by direct sunlight, marine snow, the fluorescence of biological objects, the presence of macroscopical organisms, loss of stability in divers, loss of light, artificial lighting and floating dust particles present in the water. There are numerous techniques and algorithms that may be used to restore these underwater images. This study reviews different algorithms and methods, developed in the past two decades, to give clearer ideas on the techniques present in the image restoration process, specifically for underwater images.

References

    1. 1)
      • 1. Garcia, R., Nicosevici, T., Gracias, N., et al: ‘Exploring the seafloor with underwater robots: land, sea & air’, in Lopez, A.M., Imiya, A., Pajdla, T., et al (Eds): ‘Computer vision in vehicle technology’ (John Wiley & Sons Ltd, Barcelona, 2017), pp. 7599.
    2. 2)
      • 2. Chiang, J.Y., Chen, Y.: ‘Underwater image enhancement by wavelength compensation and dehazing’, IEEE Trans. Image Process., 2012, 21, (4), pp. 17561769.
    3. 3)
      • 3. Lu, H., Li, Y., Zhang, Y.D., et al: ‘Underwater optical image processing: a comprehensive review’, Mob. Netw. Appl., 2017, 2, (1), pp. 18.
    4. 4)
      • 4. Schechner, Y.Y., Karpel, N.: ‘Recovery of underwater visibility and structure by polarization analysis’, IEEE J. Ocean. Eng., 2005, 30, (3), pp. 570587.
    5. 5)
      • 5. Treibitz, T., Schechner, Y.Y.: ‘Active polarization descattering’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (3), pp. 385399.
    6. 6)
      • 6. Zhishen, L., Tianfu, D., Gang, W.: ‘Rov based underwater blurred image restoration’, J. Ocean Univ. Qingdao, 2003, 2, (1), pp. 8588.
    7. 7)
      • 7. Tan, C.S., Sluzek, A., Jiang, T.Y.: ‘Range gated imaging system for underwater robotic vehicle’. Proc. OCEANS - Asia Pacific, Singapore, May 2007, pp. 16.
    8. 8)
      • 8. Li, H., Wang, X., Bai, T., et al: ‘Speckle noise suppression of range gated underwater imaging system’. Proc. SPIE, California, United States, September 2009, pp. 18.
    9. 9)
      • 9. Roser, M., Dunbabin, M., Geiger, A.: ‘Simultaneous underwater visibility assessment, enhancement and improved stereo’. Proc. Int. Conf. Robotics and Automation, Hong Kong, China, May 2014, pp. 18.
    10. 10)
      • 10. Anwar, S., Li, C., Porikli, F.: ‘Deep underwater image enhancement’, J. Comput. Vis. Pattern Recogn., 2018, 10, pp. 112.
    11. 11)
      • 11. Hu, Y., Wang, K., Zhao, X.: ‘Underwater image restoration based on convolutional neural network’. Proc. Asian Conf. Machine Learning, 2018, pp. 296311.
    12. 12)
      • 12. Shin, Y., Cho, Y., Pandey, G., et al: ‘Estimation of ambient light and transmission map with common convolutional architecture’. Proc. OCEANS MTS/IEEE, Monterey, USA, September 2016, pp. 110.
    13. 13)
      • 13. Lu, H., Xu, X., Kim, H.: ‘Underwater light field depth map restoration using deep convolutional neural fields’, in Lu, H., Xi, X. (Eds): ‘Artificial intelligence and robotics’ (Springer Int. publishing press, Switzerland, 2018), pp. 305312.
    14. 14)
      • 14. Wang, Y., Zhang, J., Cao, Y., et al: ‘A deep Cnn method for underwater image enhancement’. Proc. Int. Conf. Image Processing, Beijing, China, September 2017, pp. 13821386.
    15. 15)
      • 15. Fabbri, C., Islam, M.J., Sattar, J.: ‘Enhancing underwater imagery using generative adversarial networks’. Proc. Int. Conf. Robotics and Automation, Brisbane, Australia, May 2018, pp. 71597165.
    16. 16)
      • 16. Li, J., Skinner, K.A., Eustice, R.M.: ‘Watergan: unsupervised generative network to enable real-time color correction of monocular underwater images’, IEEE Robot. Autom. Lett., 2018, 3, (1), pp. 387394.
    17. 17)
      • 17. Wang, Y., Diao, M.: ‘A novel underwater image restoration algorithm’, Int. J. Performability Eng., 2018, 14, (7), pp. 15131520.
    18. 18)
      • 18. Wang, M., Zhou, S., Yan, W.: ‘Blurred image restoration using knife-edge function and optimal window Wiener filtering’, J. PLOS ONE, 2018, 13, (1), pp. 111.
    19. 19)
      • 19. Lu, H., Li, Y., Nakashima, S., et al: ‘Turbidity underwater image restoration using spectral properties and light compensation’, IEICE Trans. Inf. Syst., 2016, E99.D, (1), pp. 219227.
    20. 20)
      • 20. Sahu, P., Gupta, N., Sharma, N.: ‘A survey on underwater image enhancement techniques’, Int. J. Comput. Appl., 2014, 87, (13), pp. 1923.
    21. 21)
      • 21. Wang, G., Zheng, B., Fei Sun, F.: ‘Estimation-based approach for underwater image restoration’, J. Opt. Lett., 2011, 36, (13), pp. 23842386.
    22. 22)
      • 22. Trucco, E., Olmos-Antillon, A.T.: ‘Self-tuning underwater image restoration’, IEEE J. Ocean. Eng., 2006, 31, (2), pp. 511519.
    23. 23)
      • 23. Bazeille, S., Quidu, I., Jaulin, L., et al: ‘Automatic underwater image pre-processing’. Proc. Characterisation du Milieu Marin, France, France, October 2006, pp. 111.
    24. 24)
      • 24. Shamsuddin, N.B., Ahmad, W.F.B.W., Baharudin, B.B., et al: ‘Significance level of image enhancement techniques for underwater images’. Proc. Int. Conf. Computer & Information Science, Kuala Lampur, Malaysia, June 2012, pp. 490494.
    25. 25)
      • 25. Kaeli, J.W., Singh, H., Murphy, C.: ‘Improving color correction for underwater image surveys’. Proc. OCEANS - MTS, Waikoloa, USA, September 2011, pp. 16.
    26. 26)
      • 26. Iqbal, K., Odetayo, M., James, A., et al: ‘Enhancing the low quality images using unsupervised colour correction method’. Proc. Int. Conf. Systems, Man and Cybernetics, Istanbul, Turkey, October 2010, pp. 17031709.
    27. 27)
      • 27. Petit, F., Capelle-Laize, A., Carre, P.: ‘Underwater image enhancement by attenuation inversion with quaternions’. Proc. Int. Conf. Acoustics, Speech and Signal Processing, Taipei, Taiwan, April 2009, pp. 11771180.
    28. 28)
      • 28. Iqbal, K., Abdul Salam, R., Azam, O.: ‘Underwater image enhancement using an integrated colour model’, IAENG Int. J. Comput. Sci., 2007, 34, (2), pp. 16.
    29. 29)
      • 29. Gibson, K.B.: ‘Preliminary results in using a joint contrast enhancement and turbulence mitigation method for underwater optical imaging’. Proc. OCEANS - MTS, Washington, USA, October 2015, pp. 15.
    30. 30)
      • 30. Arnold-Bos, A., Malkasse, J.-P., Kervern, G.: ‘A preprocessing framework for automatic underwater images denoising’. Proc. European Conf. Propagation and Systems, France, Europe, 2005, pp. 18.
    31. 31)
      • 31. Arnold-Bos, A., Malkasse, J.P., Kervern, G.: ‘Towards a model-free denoising of underwater optical images’. Proc. Europe Oceans, France, June 2005, pp. 527532.
    32. 32)
      • 32. Chambah, M., Semani, D., Renouf, A., et al: ‘Underwater color constancy: enhancement of automatic live fish recognition’. Proc. Electronic Imaging, California, USA, December 2003, pp. 157168.
    33. 33)
      • 33. Fu, X., Zhuang, P., Huang, Y., et al: ‘A retinex-based enhancing approach for single underwater image’. Proc. Int. Conf. Image Processing, Paris, France, October 2014, pp. 45724576.
    34. 34)
      • 34. Ancuti, C.O., Ancuti, C.: ‘Single image dehazing by multi-scale fusion’, IEEE Trans. Image Process., 2013, 22, (8), pp. 32713282.
    35. 35)
      • 35. Ancuti, C., Ancuti, C.O., Haber, T., et al: ‘Enhancing underwater images and videos by fusion’. Proc. Int. Conf. Computer Vision and Pattern Recognition, Providence, USA, June 2012, pp. 8188.
    36. 36)
      • 36. Khan, R.: ‘Underwater image restoration using fusion and wavelet transform strategy’, J. of Comput., 2015, 10, (4), pp. 237244.
    37. 37)
      • 37. Borker, S., Bonde, S.: ‘Contrast enhancement and visibility restoration of underwater optical images using fusion’, Int. J. Intell. Eng. Syst., 2017, 10, (4), pp. 217225.
    38. 38)
      • 38. Xie, K., Pan, W., Xu, S.: ‘An underwater image enhancement algorithm for environment recognition and robot navigation’, MDPI J. Robot., 2018, 7, (13), pp. 113.
    39. 39)
      • 39. Wang, N., Qi, L., Dong, J., et al: ‘Two-stage underwater image restoration based on a physical model’. Proc. Int. Conf. Graphic and Image Processing, Tokyo, Japan, February 2017, pp. 16.
    40. 40)
      • 40. Gupta, R., Faruq, Z.: ‘Underwater image clearance using dark channel and Fft enhancement’, Int. J. Comput. Appl., 2015, 119, (23), pp. 2125.
    41. 41)
      • 41. Galdran, A., Pardo, D., Picón, A., et al: ‘Automatic red-channel underwater image restoration’, J. Vis. Commun. Image Represent., 2015, 26, pp. 132145.
    42. 42)
      • 42. Wen, H., Tian, Y., Huang, T., et al: ‘Single underwater image enhancement with a new optical model’. Proc. Int. Symp. Circuits and Systems, Beijing, China, May 2013, pp. 753756.
    43. 43)
      • 43. Drews, P.Jr., Nascimento, E.d., Moraes, F., et al: ‘Transmission estimation in underwater single images’. Proc. Int. Conf. Computer Vision Workshops, Sydney, Australia, December 2013, pp. 825830.
    44. 44)
      • 44. He, K., Sun, J., Tang, X., et al: ‘Single image haze removal using dark channel prior’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (12), pp. 23412353.
    45. 45)
      • 45. Yang, H., Chen, P., Huang, C., et al: ‘Low complexity underwater image enhancement based on dark channel prior’. Proc. Int. Conf. Innovations in Bio-inspired Computing and Applications, Shenzan, China, December 2011, pp. 1720.
    46. 46)
      • 46. Carlevaris-Bianco, N., Mohan, A., Eustice, R.M.: ‘Initial results in underwater single image dehazing’. Proc. OCEANS MTS, Seattle, USA, September 2010, pp. 18.
    47. 47)
      • 47. Liu, C., Meng, W.: ‘Removal of water scattering’. Proc. Int. Conf. Computer Engineering and Technology, Chengdu, China, April 2010, pp. 3539.
    48. 48)
      • 48. Yang, M., Sowmya, A., Wei, Z.: ‘Inshore eutrophic underwater image enhancement based on light-particles interaction descattering’. Proc. OCEANS, Aberdeen, UK, June 2017, pp. 15.
    49. 49)
      • 49. Meng, G., Wang, Y., Duan, J., et al: ‘Efficient image dehazing with boundary constraint and contextual regularization’. Proc. Int. Conf. Computer Vision, Sydney, Australia, December 2013, pp. 110.
    50. 50)
      • 50. Li, C., Guo, J., Cong, R., et al: ‘Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior’, IEEE Trans. Image Process., 2016, 25, (12), pp. 56645677.
    51. 51)
      • 51. Emberton, S., Chittka, L., Cavallaro, A.: ‘Hierarchical rank-based veiling light estimation for underwater dehazing’. Proc. British Conf. Machine Vision, Swansea, UK, September 2015, pp. 125.1125.12.
    52. 52)
      • 52. Chen, Z., Wang, H., Shen, J., et al: ‘Region-specialized underwater image restoration in inhomogeneous optical environments’, J. Optik, 2014, 125, (9), pp. 20902098.
    53. 53)
      • 53. Wong, S.L., Paramesran, R., Taguchi, A.: ‘Underwater image enhancement by adaptive gray world and differential gray-levels histogram equalization’, J. Adv. Electr. Comput. Eng., 2018, 18, (2), pp. 109116.
    54. 54)
      • 54. Gao, Y., Li, H., Wen, S.: ‘Restoration and enhancement of underwater images based on bright channel prior’, J. Math. Prob. Eng., 2016, 1, pp. 115.
    55. 55)
      • 55. Hitam, M.S., Awalludin, E.A., Yussof, W.N.J.H.W., et al: ‘Mixture contrast limited adaptive histogram equalization for underwater image enhancement’. Proc. Int. Conf. Computer Applications Technology, Sousse, Tunisia, January 2013, pp. 15.
    56. 56)
      • 56. Deperlioglu, O., Köse, U., Emre Guraksin, G.: ‘Underwater image enhancement with Hsv and histogram equalization’. Proc. Int. Conf. Advanced technologies, Antalya, Turkey, May 2018, pp. 461465.
    57. 57)
      • 57. Fattal, R.: ‘Single image dehazing’, ACM Trans. Graph., 2008, 27, (3), pp. 721729.
    58. 58)
      • 58. Zhu, Q., Mai, J., Shao, L.: ‘A fast single image haze removal algorithm using color attenuation prior’, IEEE Trans. Image Process., 2015, 24, (11), pp. 35223533.
    59. 59)
      • 59. Wu, M., Luo, K., Dang, J.: ‘Underwater image restoration using color correction and non-local prior’. Proc. OCEANS, Aberdeen, UK, June 2017, pp. 15.
    60. 60)
      • 60. Li, C., Guo, J., Chen, S., et al: ‘Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging’. Proc. Int. Conf. Image Processing, Phoenix, USA, September 2016, pp. 19931997.
    61. 61)
      • 61. Zhang, W., Li, G., Ying, Z.: ‘A new underwater image enhancing method via color correction and illumination adjustment’. Proc. Visual Communications and Image Processing, Florida, USA, December 2017, pp. 110.
    62. 62)
      • 62. Liu, Z., Yu, Y., Zhang, K., et al: ‘Underwater image transmission and blurred image restoration’, J. Opt. Eng., 2001, 40, (6), pp. 11251131.
    63. 63)
      • 63. Çelebi, A.T., Erturk, S.: ‘Visual enhancement of underwater images using empirical mode decomposition’, J. Expert Syst. Appl., 2012, 39, (1), pp. 800805.
    64. 64)
      • 64. Peng, Y., Zhao, X., Cosman, P.C.: ‘Single underwater image enhancement using depth estimation based on blurriness’. Proc. Int. Conf. Image Processing, Quebec City, Canada, September 2015, pp. 49524956.
    65. 65)
      • 65. Peng, Y., Zhao, X., Cosman, P.C.: ‘Underwater image restoration based on image blurriness and light absorption’, IEEE Trans. Image Process., 2017, 26, (4), pp. 15791594.
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