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An Efficient Edge Preserving Interpolation Method for Underwater Acoustic Image Resolution Enhancement

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Underwater acoustic images are acquired using sonar instrument that uses sound propagation to navigate and map the sea floor. The sonar devices are effectively used to create images of large area of the seabed. However, the visual perception of the object in the acoustic image depends on refraction, which is a function of changes in the speed of sound in successive layers of water. And refraction depends mainly on temperature, slightly on salinity and hydrostatic pressure. The quality and resolution of sonar imaging of the bottom depends on many other factors such as pitch, yaw and heave of the side scan sonar, the presence of volume scatterers in the water body, the distance of the sonar from the bottom and orientation of the object. Generally, the objects in an acoustic image would be of small size compared to their normal size as the distance between the sonar and object is larger. To detect and recognize the objects in the images, the resolution should be enhanced. In this paper, we propose an efficient edge preserving interpolation method for underwater acoustic image resolution enhancement which preserves the edge sharpness. The method handles the diagonal pixels in the first pass, in turn fills the horizontal and vertical pixels in the second pass. The results obtained are compared with the state-of-the-art interpolation techniques and the performance measures such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) shows an improved result.
Rocznik
Strony
267--274
Opis fizyczny
Bibliogr. 19 poz., fot., rys., tab.
Twórcy
  • Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam, Tamil Nadu, India
  • Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Kalavakkam, Tamil Nadu, India
Bibliografia
  • 1. Bae J.H., Kang B.S., Lee S.H., Yang E., Kim K.T. (2016), Bistatic ISAR image reconstruction using sparse-recovery interpolation of missing data, IEEE Transactions on Aerospace and Electronic Systems, 52(3): 1155-1167, doi: 10.1109/TAES.2016.150245.
  • 2. Chen M.J, Huang C.H, Lee W.L. (2005), A fast edge-oriented algorithm for image interpolation, Image and Vision Computing, 23(9): 791-798, doi: 10.1016/j.imavis.2005.05.005.
  • 3. Dong W., Zhang L., Shi G., Wu X. (2009), Nonlocal back-projection for adaptive image enlargement, [in:] Proceedings of 16th IEEE International Conference on Image Processing (ICIP), pp. 349-352, doi: 10.1109/ICIP.2009.5414423.
  • 4. Giachetti A., Asuni N. (2011), Real time artifactfree image interpolation, IEEE Transaction on Image Processing, 20(10): 2760-2768, doi: 10.1109/TIP.2011.2136352.
  • 5. Gonzalez R.C., Woods R.E. (2007), Digital Image Processing, 3rd. ed., Pearson, Prentice-Hall.
  • 6. Lehmann T.M., Gonner C., Spitzer K. (1999), Survey: interpolation methods in medical image processing, IEEE Transactions on Medical Imaging, 18(11): 1049-1075, doi: 10.1109/42.816070.
  • 7. Li X., Orchard M.T. (2000), New edge directed interpolation, IEEE Transactions on Image Processing, 10(10): 1521-1527, doi: 10.1109/83.951537.
  • 8. Lin C.K., Wu Y.H., Yang J.F., Liu B.D. (2015), An iterative enhanced super-resolution system with edgedominated interpolation and adaptive enhancements, EURASIP Journal on Advances in Signal Processing, 2015(1): article no. 9, doi: 10.1186/s13634-014-0190-x.
  • 9. Ling F., Foody G.M., Ge Y., Li X., Du Y. (2016), An iterative interpolation deconvolution algorithm for superresolution land cover mapping, IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7210-7222, doi: 10.1109/TGRS.2016.2598534.
  • 10. Mai Z., Rajan J., Verhoye M., Sijbers, J. (2011), Robust edge-directed interpolation of magnetic resonance images, Physics in Medicine & Biology, 56(22): 7287, doi: 10.1088/0031-9155/56/22/018.
  • 11. Meijering E. (2002), A chronology of interpolation: from ancient astronomy to modern signal and image processing, Proceedings of the IEEE, 90(3): 319-342, doi: 10.1109/5.993400.
  • 12. Priyadharsini R., Sree Sharmila T., Rajendran V. (2015), Underwater image enhancement using discrete wavelet and KL transform, [in:] Proceedings of 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 563-567, doi: 10.1109/ICATCCT.2015.7456948.
  • 13. Priyadharsini R., Sree Sharmila T., Rajendran V. (2017a), A wavelet transform based contrast enhancement method for underwater acoustic images, Multidimensional Systems and Signal Processing, 29(4): 1845-1859, doi: 10.1007/s11045-017-0533-5.
  • 14. Priyadharsini R., Sree Sharmila T., Rajendran V. (2017b), Acoustic image enhancement using Gaussian and Laplacian pyramid - a multiresolution based technique, Multimedia Tools and Applications, 77(5): 5547-5561, doi: 10.1007/s11042-017-4466-7.
  • 15. Su H., Tang L., Wu Y., Tretter D., Zhou J. (2012), Spatially adaptive block-based super-resolution, IEEE Transactions on Image Processing, 21(3): 1031-1045, doi: 10.1109/TIP.2011.2166971.
  • 16. Tam W.S., Kok C.W., Siu W.C. (2010), Modified edge-directed interpolation for images, Journal of Electronic Imaging, 19(1): 013011, doi: 10.1117/1.3358372.
  • 17. Yang J., Wright J., Huang T.S., Ma Y. (2010), Image super-resolution via sparse representation, IEEE Transactions on Image Processing, 19(11): 2861-2873, doi: 10.1109/TIP.2010.2050625.
  • 18. Zhang L., Wu X. (2006), An edge-guided image interpolation algorithm via directional filtering and data fusion, IEEE Transactions on Image Processing, 15(8): 2226-2238, doi: 10.1109/TIP.2006.877407.
  • 19. Zhou D., Shen X., Dong W. (2012), Image zooming using directional cubic convolution interpolation, IET Image Process, 6(6): 627-634, doi: 10.1049/ietipr.2011.0534.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aebe652e-d2db-42a5-bbc5-a3ea0ec221c2
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