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Remarks on noise removal in infrared images

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Noise removal in IR (Infra Red) images is very popular in recent years, however the same approach is utilized as for vision images, with no or minor changes. In this work we wanted to show the results of noise removal for selected filters. We focused on investigation of some filters normally used for image processing and their influence to IR image quality. In IR imaging the choice of filter depends mainly on the purpose of the processing, e.g. detection of small objects in complex images, edge and contour detection or removal of non-uniformity of the detector array. The performance of the selected noise reduction filters was evaluated using PSNR (Peak Signal-to-Noise Ratio) and MAE (Mean Absolute Error) quality measure, greater value of the PSNR and lower value of the MAE indicate better noise reduction. The results are shown only for few images from our database which contain over 2000 of IR images.
Słowa kluczowe
Wydawca
Rocznik
Strony
187--190
Opis fizyczny
Biblior. 2 poz., rys., wykr., wzory
Twórcy
autor
  • Silesian University of Technology, Institute of Automatic Control, Akademicka 16, 44-100 Gliwice, Poland
autor
  • Silesian University of Technology, Institute of Automatic Control, Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • [1] Maldague X.: Theory and practice of infrared technology for nondestructive testing, 1st edn. Wiley-Interscience, 2001.
  • [2] Budzan S., Wyzgolik R.: Face and eyes localization algorithm in thermal images for temperature measurement of the inner canthus of the eyes. Infrared Physics & Technology 60, 225–234, 2013.
  • [3] Zhou B., Wang S., Ma Y., Mei X., Li B., Li H., Fan F.: An IR image impulse noise suppression algorithm based on fuzzy logic. Infrared Physics & Technology 60, 346–358, 2013.
  • [4] Wang H. Y., Zhang K., Li Y. J: Anisotropic Gaussian filtering for infrared for infrared image, J. Infrared Millim. Waves 24 (2) 109–113, 2005.
  • [5] Dee-Noor B., Stern A., Yitzhaky Y., Kopeika N.: Infrared image denoising by nonlocal means filtering. Proc. of SPIE 8399, Visual Information Processing XXI, 2012.
  • [6] Lin C. L., Kuo C.W., Lai C. C., Tsai M. D., Chang Y. C., Cheng H.: A novel approach to fast noise reduction of IR image. Infrared Physics & Technology 54, 1–9, 2011.
  • [7] Rogalski A.: Infrared Detectors, 2nd edn. CRC Press, 2011.
  • [8] Aizenberg I., Butakoff C., Paliy D.: Impulsive noise removal using threshold Boolean filtering based on the impulse detecting functions. IEEE Signal Process. Lett. 12(1), 63–66, 2005.
  • [9] Garnett R., Huegerich T., Chui C., He W.: A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14(11), 1747–1754, 2005.
  • [10] Pok G., Liu Y., Nair A.S.: Selective removal of impulse noise based on homogeneity level information. IEEE Trans. Image Process. 12(1), 85–92, 2003.
  • [11] Islam S. M. R., Huang H., Liao M., Srinath N. K.: Image denoising based on wavelet for IR images corrupted by Gaussian, Poisson & Impulse noise. International Journal of Comp. Scie. and Net. Secur. 6, 59–70, 2013.
  • [12] Schulte S., De Witte V., Nachtegael M., Vand der Weken D., Kerre E.E.: Fuzzy random impulse noise reduction method. Fuzzy Set Syst. 158, 270–283, 2007.
  • [13] Nair M., Raju G.: A new fuzzy-based decision algorithm for high-density impulse noise removal. Signal, Image and Video Process. 6, 579–595, 2012.
  • [14] Lin C. L.: An approach to improve the quality of IR images of vein-patterns. Sensors 11, 11447–11463, 2011.
  • [15] Dawoud A., Alam M. S., Bal A., Loo C.: Target tracking in infrared imagery using weighted composite reference function-based decision fusion, IEEE Trans. Image Process. 15 (2) 404–410, 2006.
  • [16] Bal A., Alam M. S.:, Automatic target tracking in FLIR image sequences using intensity variation function and template modeling, IEEE Trans. Instrum. Meas. 54 (5) 846–1852, 2005.
  • [17] Silverman B. W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London, 1986.
  • [18] Smolka B., Lukac R.: Nonparametric Impulsive Noise Removal. In: Campilho, A. C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 155–162, 2004.
  • [19] Budzan S., Wyzgolik R.: Noise reduction in thermal images. Computer Vision and Graphics (ICCVG), Lecture Notes in Computer Science, vol. 8671, pp. 116-123, 2014.
  • [20] Mélange T., Nachtegael1 M., Schulte S., Kerre E. E.: A fuzzy filter for the removal of random impulse noise in image sequences. Image and Vision Computing, vol. 29, 407–419, 2011.
  • [21] Donoho D. L.: De-noising by soft-thresholding. IEEE Trans. on Infor. Theory 41(3), 613–627, 1995.
  • [22] Donoho D. L., Johnstone I.M.: Minimax Estimation via Wavelet Shrinkage. The Annals of Statistics 26(3), 879–921, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6a63485c-1dbe-4a00-83e2-4449e3e66210
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