Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Dehazing of remote sensing images using fourth-order partial differential equations based trilateral filter

Remote sensing images taken in hazy situations are degraded by scattering of atmospheric particles, which greatly influences the efficiency of visual systems. Therefore, the visibility restoration of hazy images becomes a significant area of research. In this study, a fourth-order partial differential equations based trilateral filter (FPDETF) dehazing approach is proposed to enhance the coarse estimated atmospheric veil. FPDETF is able to reduce halo and gradient reversal artefacts. It also preserves the radiometric information of haze-free images. The visibility restoration phase is also refined to reduce the colour distortion of dehazed images. The proposed technique has been evaluated on ten well-known remote sensing images and also compared with seven well-known existing dehazing approaches. The experimental results reveal that the proposed technique outperforms others in terms of contrast gain and percentage of saturated pixels.

References

    1. 1)
      • 36. QUICKBIRD: ‘Global land cover facility’, http://glcf.umd.edu/, accessed December 2016.
    2. 2)
      • 33. Serikawa, S., Lu, H.: ‘Underwater image dehazing using joint trilateral filter’, Comput. Electr. Eng., 2014, 40, (1), pp. 4150.
    3. 3)
      • 11. Lee, S., Yun, S., Nam, J.-H., et al: ‘A review on dark channel prior based image dehazing algorithms’, EURASIP J. Image Video Process., 2016, 2016, (1), p. 4.
    4. 4)
      • 35. Gilboa, G., Sochen, N., Zeevi, Y.Y.: ‘Image enhancement and denoising by complex diffusion processes’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (8), pp. 10201036.
    5. 5)
      • 22. Ling, Z., Fan, G., Wang, Y., et al: ‘Learning deep transmission network for single image dehazing’. IEEE Int. Conf. on Image Processing (ICIP), 2016, 2016, pp. 22962300.
    6. 6)
      • 23. Lu, H., Li, Y., Nakashima, S., et al: ‘Single image dehazing through improved atmospheric light estimation’, Multimedia Tools Appl., 2016, 75, (24), pp. 1708117096.
    7. 7)
      • 18. 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.
    8. 8)
      • 3. Xu, H., Guo, J., Liu, Q., et al: ‘Fast image dehazing using improved dark channel prior’. 2012 IEEE Int. Conf. on Information Science and Technology, 2012, pp. 663667.
    9. 9)
      • 7. Kong, N.S.P., Ibrahim, H.: ‘Color image enhancement using brightness preserving dynamic histogram equalization’, IEEE Trans. Consum. Electron., 2008, 4, (4), pp. 19621968.
    10. 10)
      • 6. Kim, T., Paik, J.: ‘Adaptive contrast enhancement using gain-controllable clipped histogram equalization’, IEEE Trans. Consum. Electron., 2008, 54, (4), pp. 18031810.
    11. 11)
      • 42. Hautiere, N., Tarel, J.-P., Aubert, D., et al: ‘Blind contrast enhancement assessment by gradient ratioing at visible edges’, Image Anal. Stereol., 2011, 27, (2), pp. 8795.
    12. 12)
      • 40. Singh, D., Kumar, V.: ‘Dehazing of remote sensing images using improved restoration model based dark channel prior’, Imaging Sci. J., 2017, 65, (5), pp. 111.
    13. 13)
      • 5. Kim, M., Chung, M.G.: ‘Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement’, IEEE Trans. Consum. Electron., 2008, 54, (3), pp. 13891397.
    14. 14)
      • 4. Du, Y., Guindon, B., Cihlar, J.: ‘Haze detection and removal in high resolution satellite image with wavelet analysis’, IEEE Trans. Geosci. Remote Sens., 2002, 40, (1), pp. 210217.
    15. 15)
      • 10. He, K., Sun, J., Tang, X.: ‘Single image haze removal using dark channel prior’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (12), pp. 23412353.
    16. 16)
      • 31. Chen, B.H., Huang, S.C., Cheng, F.C.: ‘A high-efficiency and high-speed gain intervention refinement filter for haze removal’, J. Disp. Technol., 2016, 12, (7), pp. 753759.
    17. 17)
      • 8. Sengee, N., Sengee, A., Choi, H.-K.: ‘Image contrast enhancement using bi-histogram equalization with neighborhood metrics’, IEEE Trans. Consum. Electron., 2010, 56, (4), pp. 27272734.
    18. 18)
      • 12. Ding, M., Tong, R.: ‘Efficient dark channel based image dehazing using quadtrees’, Sci. China Inf. Sci., 2013, 56, (9), pp. 19.
    19. 19)
      • 32. Tarel, J.-P., Hautiere, N.: ‘Fast visibility restoration from a single color or gray level image’. 2009 IEEE 12th Int. Conf. on Computer Vision, 2009, pp. 22012208.
    20. 20)
      • 29. Zhang, M., Gunturk, B.K.: ‘Multiresolution bilateral filtering for image denoising’, IEEE Trans. Image Process., 2008, 17, (12), pp. 23242333.
    21. 21)
      • 25. Lu, H., Li, Y., Xu, X., et al: ‘Underwater image descattering and quality assessment’. IEEE Int. Conf. on Image Processing (ICIP), 2016, 2016, pp. 19982002.
    22. 22)
      • 26. Xie, C.-H., Qiao, W.-W., Liu, Z., et al: ‘Single image dehazing using kernel regression model and dark channel prior’, Signal Image Video Process., 2017, 11, (4), pp. 705712.
    23. 23)
      • 16. Ding, M., Wei, L.: ‘Single-image haze removal using the mean vector l2-norm of rgb image sample window’, Optik, Int. J. Light Electron Opt., 2015, 126, (23), pp. 35223528.
    24. 24)
      • 15. Li, J., Zhang, H., Yuan, D., et al: ‘Single image dehazing using the change of detail prior’, Neurocomputing, 2015, 156, pp. 111.
    25. 25)
      • 9. Rajavel, P.: ‘Image dependent brightness preserving histogram equalization’, IEEE Trans. Consum. Electron., 2010, 56, (2), pp. 756763.
    26. 26)
      • 27. Zhang, S., Wang, T., Dong, J., et al: ‘Underwater image enhancement via extended multiscale retinex’, Neurocomputing, 2017, 245, pp. 19.
    27. 27)
      • 30. Narasimhan, S.G., Nayar, S.K.: ‘Contrast restoration of weather degraded images’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (6), pp. 713724.
    28. 28)
      • 38. MODIS: ‘Moderate-resolution imaging spectroradiometer’, https://modis.gsfc.nasa.gov/gallery/showall.php, accessed December 2016.
    29. 29)
      • 20. Cai, B., Xu, X., Jia, K., et al: ‘Dehazenet: an end-to-end system for single image haze removal’, IEEE Trans. Image Process., 2016, 25, (11), pp. 51875198.
    30. 30)
      • 17. Li, Z., Zheng, J., Zhu, Z., et al: ‘Weighted guided image filtering’, IEEE Trans. Image Process., 2015, 24, (1), pp. 120129.
    31. 31)
      • 13. Zhao, H., Xiao, C., Yu, J., et al: ‘Single image fog removal based on local extrema’, IEEE/CAA J. Autom. Sin., 2015, 2, (2), pp. 158165.
    32. 32)
      • 24. Li, Y., Lu, H., Li, J., et al: ‘Underwater image de-scattering and classification by deep neural network’, Comput. Electr. Eng., 2016, 54, pp. 6877.
    33. 33)
      • 34. You, Y.-L., Kaveh, M.: ‘Fourth-order partial differential equations for noise removal’, IEEE Trans. Image Process., 2000, 9, (10), pp. 17231730.
    34. 34)
      • 41. Singh, D., Kumar, V.: ‘Modified gain intervention filter based dehazing technique’, J. Mod. Opt., 2017, 64, (20), pp. 114.
    35. 35)
      • 37. IKONOS: ‘Ikonos satellites’, http://www.geoimage.com.au/satellite/ikonos, accessed December 2016.
    36. 36)
      • 14. Ge, G., Wei, Z., Zhao, J.: ‘Fast single-image dehazing using linear transformation’, Optik, Int. J. Light Electron Opt., 2015, 126, (21), pp. 32453252.
    37. 37)
      • 2. Yang, H.-Y., Chen, P.-Y., Huang, C.-C., et al: ‘Low complexity underwater image enhancement based on dark channel prior’. Second Int. Conf. on Innovations in Bio-inspired Computing and Applications (IBICA), 2011, 2011, pp. 1720.
    38. 38)
      • 21. Ren, W., Liu, S., Zhang, H., et al: ‘Single image dehazing via multi-scale convolutional neural networks’. European Conf. on Computer Vision, 2016, pp. 154169.
    39. 39)
      • 39. Tripathi, A.K., Mukhopadhyay, S.: ‘Removal of fog from images: a review’, IETE Tech. Rev., 2012, 29, (2), pp. 148156.
    40. 40)
      • 19. Fan, X., Wang, Y., Tang, X., et al: ‘Two-layer Gaussian process regression with example selection for image dehazing’, IEEE Trans. Circuits Syst. Video Technol., 2016, PP, (99), pp. 11.
    41. 41)
      • 28. Long, J., Shi, Z., Tang, W., et al: ‘Single remote sensing image dehazing’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (1), pp. 5963.
    42. 42)
      • 1. Fu, X., Wang, J., Zeng, D., et al: ‘Remote sensing image enhancement using regularized-histogram equalization and dct’, IEEE Geosci. Remote Sens. Lett., 2015, 12, (11), pp. 23012305.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0044
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0044
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address