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Keywords:
Image processing; linear heat equation; finite volume method; adaptivity; SAR image; speckle noise
Image processing; linear heat equation; finite volume method; adaptivity; SAR image; speckle noise
Summary:
In this paper we present a method to remove the noise by applying the Perona Malik algorithm working on an irregular computational grid. This grid is obtained with a quad-tree technique and is adapted to the image intensities—pixels with similar intensities can form large elements. We apply this algorithm to remove the speckle noise present in SAR images, i.e., images obtained by radars with a synthetic aperture enabling to increase their resolution in an electronic way. The presence of the speckle in an image degrades the quality of the image and makes interpretation of features more difficult. Our purpose is to remove this noise to such a degree that the edge detection or landscape elements detection can be performed with relatively simple tools. The progress of smoothing leads to grids with significantly less number of elements than the original number of pixels. The results are compared with measurements performed on an inspected area of interest. At the end we show the possibility to modify the scheme to the adaptive mean curvature flow filter which can be used to smooth the boundaries.
References:
[1] Bänsch, E., Mikula, K.: A coarsening finite element strategy in image selective smoothing. Computing and Visualization in Science 1 (1997), 53–61. DOI 10.1007/s007910050005 | Zbl 0971.65084
[2] Canny, J.: A computational approach to edge detection, in pattern analysis and machine intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence ol.PAMI-8, 6 (1986), 679–698. DOI 10.1109/TPAMI.1986.4767851
[3] Catté, F., Lions, P. L., Morel, J. M., Coll, T.: Image selective smoothing and edge detectizon by nonlinear diffusion. SIAM J. Numer. Anal. 29 (1992), 182–193. DOI 10.1137/0729012 | MR 1149092
[4] Eymard, R., Gallouët, T., Herbin, R.: Finite volume method. In: Handbook for Numerical Analysis 7, Elsevier, Amsterdam, 2000, 713–1020. MR 1804748
[5] Eymard, R., Handlovičová, A., Mikula, K.: Approximation of nonlinear parabolic equations using a family of conformal and non-conformal schemes. Communications in Pure and Applied Analysis 11 (2012), 147–172. DOI 10.3934/cpaa.2012.11.147 | MR 2833342 | Zbl 1264.65159
[6] Eymard, R., Handlovičová, A., Mikula, K.: Study of a finite volume scheme for the regularized mean curvature ow level set equation. IMA Journal of Numerical Analysis 31 (2011), 813–846. DOI 10.1093/imanum/drq025 | MR 2832781
[7] Krivá, Z., Mikula, K.: An adaptive finite volume scheme for solving non-linear diffusion equations in image processing. J. Visual Communication and Image Representation 13 (2002), 22–35. DOI 10.1006/jvci.2001.0502
[8] Krivá, Z., Mikula, K.: Adaptive diamond cell finite volume method in image processing. In: Proc. Algoritmy 2009, Conf. on Scientific Computing (2009), 174–188. Zbl 1173.94322
[9] Krivá, Z., Mikula, K.: A model and numerical scheme for processing of color images. Journal of Electrical Engineering FEI STU 12 (2000), 21–25. Zbl 0979.35068
[10] Lee, J.-S., Pottier, L.: Polarimetric Radar Imaging: from Basics to Applications. CRC Press, Boca Raton, 2009.
[11] Osher, S., Sethian, J. A.: Front propagating with curvature dependent speed: algorithms based on the Hamilton–Jacobi formulation. J. Comput. Phys. 29 (1988), 12–49. DOI 10.1016/0021-9991(88)90002-2 | MR 0965860
[12] Papčo, J., Bakoň, M.: Object Recognition Based on High Resolution Radar Imagery. German Aerospace Center (DLR), TSX-Archive-2012 project ID LAN1583, 2012.
[13] Airbus, Defence and Space: TerraSAR-X Image Product Guide, Basic and Enhanced Radar Satellite Imagery. ( http://www2.geo-airbusds.com/files/pmedia/public/r459 9 201408 tsxx-itd-ma-0009 tsx-productguide i2.00.pdf), 2014.
[14] Trubač, M.: Adaptívne algoritmy metódy konečných objemov aplikované na analýzu viackanálového obrazu. Diploma thesis, STU, Bratislava, 2014.
[15] Vanko, J.: Monitoring Landscape Changes Using Satellite Radar Imagery. Diploma thesis, STU, Bratislava, 2015.
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