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Archives of Computational Methods in Engineering

, Volume 26, Issue 1, pp 143–161 | Cite as

An Overview of Multiresolution Analysis for Nondestructive Evaluation of Pavement Surface Drainage

  • Behrouz Mataei
  • H. ZakeriEmail author
  • F. Moghadas Nejad
Original Paper
  • 84 Downloads

Abstract

Network level drainage assessment of the pavement surface plays a crucial role in controlling and decreasing the accident rate. Hydroplaning is one of the major causes of accidents in wet weather conditions and is the consequence of low drainage quality of pavement surfaces. Since no automated system currently exists for the pavement drainage evaluation, this work was conducted to present a new system to assess drainage process quality. For this aim, the saturation situation was simulated for pavement surface and photo acquisition was carried out on the drainage process of pavement surface after saturation. Finally, image processing method was applied to produce an index related to drainage quality. Using a proper method to enhance and prepare these images for the analysis step and find appreciate feature for the drainage quality is also among the necessities of drainage assessment. After a brief overview of multiresolution analysis, we revise the state-of-the-art of multiresolution analysis methods by discussing assessing parameters for asphalt surface image enhancement in nondestructive evaluation, formulated and fused to allow for a general comparison. In this work, different transform methods are used for asphalt surface image enhancement and a comparison is made between wavelet, curvelet, ridgelet, shearlet, and contourlet transforms by assessing parameters including TIME, PSNR, SNR, MSE, MAE, MSE, UQI, and SSIM. The comparison among the obtained results shows the superiority of shearlet transform over other transforms in providing of processed images with higher quality. Furthermore, it was found that ridgelet transform is more suitable for the jobs which time is the main parameter.

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Bhutada G, Anand R, Saxena S (2011) Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform. Digital Signal Process 21(1):118–130CrossRefGoogle Scholar
  2. 2.
    Jaiswal A, Upadhyay J, Somkuwar A (2014) Image denoising and quality measurements by using filtering and wavelet based techniques. AEU-Int J Electron Commun 68(8):699–705CrossRefGoogle Scholar
  3. 3.
    Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. arXiv preprint arXiv 1003:4053Google Scholar
  4. 4.
    Hedaoo P, Godbole SS (2011) Wavelet thresholding approach for image denoising. Int J Netw Secur Appl (IJNSA) 3(4):16–21Google Scholar
  5. 5.
    Rosenfeld A, Kak AC (2014) Digital picture processing. vol. 1. Elsevier, AmsterdamzbMATHGoogle Scholar
  6. 6.
    Jain AK (1989) Fundamentals of digital image processing. Prentice-Hall, Inc, New DelhizbMATHGoogle Scholar
  7. 7.
    Ji T-L, Sundareshan MK, Roehrig H (1994) Adaptive image contrast enhancement based on human visual properties. IEEE Trans Med Imaging 13(4):573–586CrossRefGoogle Scholar
  8. 8.
    Agaian S (1990) Advances and problems of the fast orthogonal transforms for signal-images processing applications (part 1). Pattern recognition, classification, forecasting. Yearbook, The Russian Academy of Sciences, Nauka, Moscow, pp 146–215Google Scholar
  9. 9.
    Agaian S (1991) Advances and problems of fast orthogonal transform for signal/image processing applications. Part 1:146–215Google Scholar
  10. 10.
    Aghagolzadeh S, Ersoy OK (1992) Transform image enhancement. Opt Eng 31(3):614–626CrossRefGoogle Scholar
  11. 11.
    Wang DC, Vagnuccin AH, Li C (1983) Digital image enhancement: a survey. Comput Vis Gr Image Process 24(3):363–381CrossRefGoogle Scholar
  12. 12.
    Morrow WM et al (1992) Region-based contrast enhancement of mammograms. IEEE Trans Med Imaging 11(3):392–406CrossRefGoogle Scholar
  13. 13.
    Beghdadi A, Le Negrate A (1989) Contrast enhancement technique based on local detection of edges. Comput Vis Gr Image Process 46(2):162–174CrossRefGoogle Scholar
  14. 14.
    Gonzalez RC, Woods RE (2002) Digital image processing. Prentice hall, Upper Saddle RiverGoogle Scholar
  15. 15.
    Grigoryan AM, Agaian SS (2004) Transform-based image enhancement algorithms with performance measure. Adv Imaging Electron Phys 130:165–242CrossRefGoogle Scholar
  16. 16.
    Agaian SS, Panetta K, Grigoryan AM (2001) Transform-based image enhancement algorithms with performance measure. IEEE Trans Image Process 10(3):367–382CrossRefzbMATHGoogle Scholar
  17. 17.
    Starck, J-L, Elad M, Donoho D (2004) Redundant multiscale transforms and their application for morphological component separation. Adv Imaging Electron Phys. 132(82):287–348CrossRefGoogle Scholar
  18. 18.
    Li J (2003) A wavelet approach to edge detection. Sam Houston State University, HuntsvilleGoogle Scholar
  19. 19.
    Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41(7):909–996MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693CrossRefzbMATHGoogle Scholar
  21. 21.
    Mallat SG, Zhong S (1989) Complete signal representation with multiscale edges. New York University, Courant Institute of Mathematical Sciences, Computer Science Division, New YorkGoogle Scholar
  22. 22.
    Piao Y, Shin L-H, Park H (2007) Image resolution enhancement using inter-subband correlation in wavelet domain. In: Image Processing, 2007. IEEE International Conference on ICIP 2007.Google Scholar
  23. 23.
    Demirel H, Anbarjafari G (2010) Satellite image resolution enhancement using complex wavelet transform. IEEE Geosci Remote Sens Lett 7(1):123–126CrossRefGoogle Scholar
  24. 24.
    Atkins CB, Bouman CA, Allebach JP (2001) Optimal image scaling using pixel classification. in Image Processing, 2001. Proceedings. 2001 International Conference on. 2001. IEEE.Google Scholar
  25. 25.
    Carey WK, Chuang DB, Hemami SS (1999) Regularity-preserving image interpolation. IEEE Trans Image Process 8(9):1293–1297CrossRefGoogle Scholar
  26. 26.
    Mallat S (1999) A wavelet tour of signal processing, (Wavelet analysis & its applications)Google Scholar
  27. 27.
    Candes EJ (1998) Ridgelets: theory and applications, Stanford University, StanfordGoogle Scholar
  28. 28.
    Candès EJ, Donoho DL (1999) Ridgelets: a key to higher-dimensional intermittency? Philos Trans R Soc Lond A 357(1760):2495–2509MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Deans SR (2007) The Radon transform and some of its applications. Courier Corporation, North ChelmsfordzbMATHGoogle Scholar
  30. 30.
    Bolker ED (1987) The finite Radon transform. Contemp Math 63:27–50MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Campisi P, Neri A, Scarano G (2002) Model based rotation-invariant texture classification. In: Proceedings. 2002 International Conference on Image Processing (IEEE).Google Scholar
  32. 32.
    Candes EJ, Donoho DL (2000) Curvelets: a surprisingly effective nonadaptive representation for objects with edges. DTIC DocumentGoogle Scholar
  33. 33.
    Guo K et al (2006) Wavelets with composite dilations and their MRA properties. Applied and Computational Harmonic. Analysis 20(2):202–236MathSciNetzbMATHGoogle Scholar
  34. 34.
    Labate D et al (2005) Sparse multidimensional representation using shearlets. In Optics & Photonics 2005. International Society for Optics and Photonics.Google Scholar
  35. 35.
    Guo K, Labate D (2007) Optimally sparse multidimensional representation using shearlets. SIAM J Math Anal 39(1):298–318MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Do M, Vetterli M (2003) Contourlets. In: Stoeckler J, Welland GV, (eds) Beyond wavelet, Academic Press, New YorkGoogle Scholar
  37. 37.
    Do M, Vetterli M (2003) Contourlet: A computational framework for directional multiresolution image representation. In IEEE Trans. Image Proc.Google Scholar
  38. 38.
    Do MN (2001) D.M.I., Representations [Ph. D. dissertation]. Swiss Federal Institute of TechnologyGoogle Scholar
  39. 39.
    Eslami R, Radha H.(2003) On low bit-rate coding using the contourlet transform. In Signals, Systems and Computers, 2004. Conference record of the thirty-seventh Asilomar Conference on. 2003. IEEE.Google Scholar
  40. 40.
    Gonzalez R, Woods R (2008) Digital image processing. Pearson prentice hall, Upper Saddle RiverGoogle Scholar
  41. 41.
    Pratt WK (2009) Digital image processing. 3rd edn. Wiley, New YorkGoogle Scholar

Copyright information

© CIMNE, Barcelona, Spain 2017

Authors and Affiliations

  1. 1.Department of EngineeringRazi UniversityKermanshahIran
  2. 2.Department of Civil and Environment EngineeringAmirkabir University of TechnologyTehranIran

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