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

access icon free Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms

© The Institution of Engineering and Technology
Received 08/04/2013, Accepted 09/07/2013, Revised 04/07/2013, Published

In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm-based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques.

Inspec keywords: image denoising; remote sensing; evolutionary computation; particle swarm optimisation

Other keywords: PSO technique; signal-to-noise ratio; ABC technique; evolutionary algorithm; artificial bee colony technique; edge keeping index; satellite image denoising; stochastic global optimisation technique; improved subband adaptive thresholding function; peak signal-to-noise ratio; edge preservation index; Cuckoo search algorithm; particle swarm optimisation; CS algorithm

Subjects: Optical, image and video signal processing; Optimisation techniques; Computer vision and image processing techniques; Optimisation techniques

References

    1. 1)
      • 17. Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: ‘Image denoising using Gaussian scale mixtures in the wavelet domain’, IEEE Trans. Image Process., 2003, 12, pp. 1338–1351 (doi: 10.1109/TIP.2003.818640).
    2. 2)
      • 19. Rabbani, H., Mansur, V., Purang, A., Saeed, G.: ‘Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors’, IEEE Trans. Biomed. Eng., 2008, 55, (9), pp. 2152–2160 (doi: 10.1109/TBME.2008.923140).
    3. 3)
      • 11. Achim, A., Kuruoghlu, E.: ‘Image denoising using alpha-stable distributions in the complex wavelet domain’, IEEE Signal Process. Lett., 2005, 12, (1), pp. 17–20 (doi: 10.1109/LSP.2004.839692).
    4. 4)
      • 23. Zhang, X.P.: ‘State-scale adaptive noise reduction in images based on thresholding neural network’. Proc. IEEE Int. Conf. on Acoustic, Speech and Signal Processing, 2001, pp. 1889–1892.
    5. 5)
      • 1. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Pearson Prentice-Hall, Singapore, 2002, 2nd edn.).
    6. 6)
      • 10. Achim, A., Bezerianos, A., Tsakalides, P.: ‘Novel Bayesian multiscale method for speckle removal in medical ultrasound Images’, IEEE Trans. Med. Imaging, 2001, 20, (8), pp. 772–783 (doi: 10.1109/42.938245).
    7. 7)
      • 22. Zhang, X.P., Desai, M.D.: ‘Adaptive denoising based on SURE risk’, IEEE Signal Process.’, Lett., 1998, 5, (10), pp. 265–267 (doi: 10.1109/97.720560).
    8. 8)
      • 3. Mallat, G.: ‘Theory for multi-resolution signal decomposition: the wavelet representation’, IEEE Trans. Pattern Anal. Mach. Intell., 1989, 2, (7), pp. 674–694 (doi: 10.1109/34.192463).
    9. 9)
      • 2. Bhandari, A.K., Kumar, A., Padhy, P.K.: ‘Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing’. IET Signal Process., 2012, pp. 1–9.
    10. 10)
      • 13. Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: ‘Wavelet-based statistical signal processing using hidden Markov models’, IEEE Trans. Signal Process., 1998, 46, (4), pp. 886–902 (doi: 10.1109/78.668544).
    11. 11)
      • 5. Donoho, D.L., Johnstone, I.M.: ‘Adapting to unknown smoothness via wavelet shrinkage’, J. Am. Stat. Assoc., 1995, 90, (432), pp. 1200–1224 (doi: 10.1080/01621459.1995.10476626).
    12. 12)
      • 30. Bhandari, A.K., Gadde, M., Kumar, A., Singh, G.K.: ‘Comparative analysis of different wavelet filters for low contrast and brightness enhancement of multispectral remote sensing images’. Proc. IEEE Int. Conf. on Machine Vision and Image Processing (MVIP), 2012, pp. 81–86.
    13. 13)
      • 28. Nasri, M., Pour, H.N.: ‘Image denoising in the wavelet domain using a new adaptive thresholding function’, Elsevier J. Neurocomput., 2009, 72, pp. 1012–1025 (doi: 10.1016/j.neucom.2008.04.016).
    14. 14)
      • 29. Bhutada, G.G., Anand, R.S., Saxena, S.C.: ‘Image enhancement by wavelet-based thresholding neural network with adaptive learning rate’, IET Image Process., 2011, 5, (7), pp. 573–582 (doi: 10.1049/iet-ipr.2010.0014).
    15. 15)
      • 38. Mu, A.Q., De-Xin, C., Wang, X.H.: ‘A modified particle swarm optimization algorithm’, Nat. Sci., 2009, 1, (2), pp. 151–155.
    16. 16)
      • 16. Bhuiyan, M.I.H., Ahmad, M.O., Swamy, M.N.S.: ‘New spatial adaptive wavelet based method for the despeckling of medical ultrasound image’. Proc. IEEE Int. Conf. on Symp. on Circuits and System, 2007, pp. 2347–2350.
    17. 17)
      • 7. Gao, H., Bruce, A.G.: ‘WaveShrink with firm shrinkage’, Stat. Sin., 1997, 7, pp. 855–874.
    18. 18)
      • 8. Gao, H.: ‘Wavelet shrinkage denoising using the nonnegative garrote’, J. Comput. Graph. Stat., 1998, 7, pp. 469–488.
    19. 19)
      • 14. Mohamad, M., Hamid, M.: ‘Ultrasound speckle suppression using heavy tailed distribution in the dual tree complex wavelet domain’. Proc. IEEE Int. Conf. on Wavelet Diversity and Design, 2007, pp. 65–68.
    20. 20)
      • 32. Bhadauria, H.S., Dewal, M.L., Anand, R.S.: ‘Comparative analysis of curvelet based techniques for denoising of computed tomography images’. Devices and Communications (ICDeCom), Int. Conf. on. IEEE, 2011, pp. 1–5.
    21. 21)
      • 31. Foi, A., Katkovnik, V., Egiazarian, K.: ‘Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images’, IEEE Trans. Image Process., 2007, 16, (5), pp. 1395–1411 (doi: 10.1109/TIP.2007.891788).
    22. 22)
      • 36. Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: ‘Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients’, IEEE Trans. Evol. Comput., 2004, 8, pp. 240–255 (doi: 10.1109/TEVC.2004.826071).
    23. 23)
      • 34. Bhutada, G.G., Anand, R.S., Saxena, S.C.: ‘Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform’, Digital Signal Process., 2011, 21, (1), pp. 118–130 (doi: 10.1016/j.dsp.2010.09.002).
    24. 24)
      • 25. Liu, P., Huang, F., Li, G., Liu, Z.: ‘Remote-sensing image denoising using partial differential equations and auxiliary images as priors’, Geosci. Remote Sens. Lett., IEEE, 2012, 9, (3), pp. 358–362 (doi: 10.1109/LGRS.2011.2168598).
    25. 25)
      • 26. Sivakumar, R., Balaji, G., Ravikiran, R.S.J., Karikalan, R., Janaki, S.S.: ‘Image denoising using contourlet transform’. Proc. IEEE Second Int. Conf. in Computer and Electrical Engineering, 2009, vol. l, no. 1, pp. 22–25.
    26. 26)
      • 18. Pizurica, A., Philips, W.: ‘Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising’, IEEE Trans. Image Process., 2006, 15, (3), pp. 654–665 (doi: 10.1109/TIP.2005.863698).
    27. 27)
      • 27. Yu-feng, L.I.: ‘Bayesian Denoising for remote sensing image based on undecimated discrete wavelet transform’, Proc. IEEE Int. Conf. on Information Engineering and Computer science (ICIECS), 2009, 3, pp. 1–4.
    28. 28)
      • 6. Donoho, D.L.: ‘Denoising by soft thresholding’, IEEE Trans. Inf. Theory, 1995, 41, pp. 613–627 (doi: 10.1109/18.382009).
    29. 29)
      • 40. Yang, X.S., Deb, S.: ‘Cuckoo search via Lévy flights’. World Congress on Nature & Biologically Inspired Computing (NaBIC 2009). IEEE Publications, 2009, pp. 210–214, http://papercore.org/Yang2009.
    30. 30)
      • 12. Mihcak, M.K., Kozintsev, I., Ramchandran, K., Moulin, P.: ‘Low complexity image denoising based on statistical modeling of wavelet coefficients’, IEEE Signal Process. Lett., 1999, 9, pp. 300–303 (doi: 10.1109/97.803428).
    31. 31)
      • 37. Gupta, S., Devi, S.: ‘Modified PSO algorithm with high exploration’, Int. J. Softw. Eng. Res. Pract., 2011, 1, (1), pp. 15–19.
    32. 32)
      • 39. Karaboga, D.: ‘An idea based on honey bee swarm for numerical optimization’. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
    33. 33)
      • 24. Zhang, X.P.: ‘Thresholding neural network for adaptive noise reduction’, IEEE Trans. Neural Netw., 2001, 12, (3), pp. 567–584 (doi: 10.1109/72.925559).
    34. 34)
      • 15. Michailovich, O.V., Tannenbum, A.: ‘Despeckling of ultrasound images’, IEEE Trans. Ultrason., Ferroelectr. Freq. Control, 2006, 53, (1), pp. 64–78 (doi: 10.1109/TUFFC.2006.1588392).
    35. 35)
      • 9. Fodder, I.K., Kamath, C.: ‘Denoising through wavelet shrinkage: an empirical study’, J. Electron. Imaging, 2003, 12, (1), pp. 151–160 (doi: 10.1117/1.1525793).
    36. 36)
      • 35. Bhutada, G.G., Anand, R.S., Saxena, S.C.: ‘Fast adaptive learning algorithm for sub-band adaptive thresholding function in image denoising’, Int. J. Comput. Intell. Stud., 2010, 1, (3), pp. 227–241 (doi: 10.1504/IJCISTUDIES.2010.034887).
    37. 37)
      • 41. Prattt, W.K.: ‘Digital image processing’ (John Wiley and Sons, 2006, 3rd edn.).
    38. 38)
      • 4. Donoho, D.L., Johnstone, I.M.: ‘Ideal spatial adaptation by wavelet shrinkage’, Biometrika, 1994, 81, (3), pp. 425–455 (doi: 10.1093/biomet/81.3.425).
    39. 39)
      • 33. Bhutada, G.G., Anand, R.S., Saxena, S.C.: ‘PSO-based learning of sub-band adaptive thresholding function for image denoising’, Springer Signal, Image Video Process. (SIViP), 2012, 6, pp. 1–7 (doi: 10.1007/s11760-010-0167-7).
    40. 40)
      • 21. Yu, H., Zhao, L., Wang, H.: ‘Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain’, IEEE Trans. Image Proc., 2009, 18, (10), pp. 2364–2369 (doi: 10.1109/TIP.2009.2026685).
    41. 41)
      • 20. Rabbani, H.: ‘Image denoising in steerable pyramid domain based on a local Laplace prior’, Elsevier J. Pattern Recognit., 2009, 42, (9), pp. 2181–2193 (doi: 10.1016/j.patcog.2009.01.005).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0139
Loading

Related content

content/journals/10.1049/iet-spr.2013.0139
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address